Fielding a SHM System for an Aged Military Aircraft
Aging military aircraft require an increasing amount of structural inspections to safely maintain them. In a case with Norwegian Defence Sea King search and rescue helicopters, required X-ray and Eddy Current inspections on two of the Main Gearbox Attachment Lugs threatened to ground the fleet. The lugs are made of casted aluminum and subsequently have a very short critical crack length. After extending the use of the helicopters beyond its safe life of 10000 flight hours, crack lengths became critical faster than the OEM recommended inspection intervals. The SMART layer sensor network was installed on the remaining fleet of helicopters while they were in for heavy maintenance. With the help of hardware for data acquisition and analyzing software, the RNoAF technicians have been able to inspect the cracks at short intervals. Acellent have verified the data integrity and reported crack status. With the Acellent SHM system installed in the remaining active fleet of Sea Kings, the following requirements where adhered to: • The installation did not need any certification • The sensors did not cover the damaged area • Time used for inspection is well beyond 10 minutes • The installation did not require any design changes • Applying the sensors did not increase normal depot downtime.
Structural Health Monitoring –The Key Enabler of Condition Based Maintenance in Aviation
The Advisory Council for Aeronautical Research in Europe (ACARE) envisages that, by 2050, all new aircraft will be designed for condition-based maintenance (CBM). This will result in a significant 40% reduction in Maintenance Repair & Overhaul (MRO) process time and costs, increase in aircraft availability, and maximization of asset utilization. The backbone of CBM is the continuous monitoring of the aircraft performance utilizing permanently installed sensors. Naturally, Structural Health Monitoring (SHM) plays an essential role on the successful implementation of CBM as it provides the needed information for structural assessment of critical aircraft structures. This paper discusses how SHM fits into the framework of CBM and highlights the results of the European project ReMAP – Real-time CBM for adaptive Aircraft Maintenance Planning. More specifically, the consortium efforts for multi-sensing SHM system integration, data synchronization and information fusion will be presented, while emphasis will be given into the conceptual design of a SHM system that is capable of damage anomaly detection, global location identification, damage type assessment, damage severity and prognostics. Innovative data-driven machine-learning algorithms were developed during the project which enabled health diagnostics and prognostics tasks of primary structures using data collected during tests at lower structural levels. This talk will demonstrate that hierarchical testing of SHM systems and scale-up approaches are a key for putting SHM into practice and for making steps towards CBM.
Computation-enabled Digital Twin in the Built Environment
This study makes an original attempt to equip a digital twin (DT) for an information construct with computational capabilities to achieve an informationcomputation construct in the built environment. This attempt is achieved by developing a DT methodology for a university campus environment, enabling a twin representation of real-world construction with spatiotemporal analysis in multiple scales, integrating computation, information, and machine learning models into a cyber-physical-social system (buildings, infrastructure, and affected community) for seamless decision-making from design through construction to operation phases, and evaluating structural behaviors under extreme loads. Potential value of the campusscale DT includes the understanding of student aggregation, traffic flow, structural stability, building constructability, damage/cost scenario of existing and new buildings, and community impacts in the wake of a postulated earthquake event.
Physics Enhanced Machine Learning for Monitoring and Twinning
To ensure a resource-efficient and resilient operation of engineered systems, it is imperative to understand their performance as-is; a task which can be effectuated through Structural Health Monitoring (SHM). When considering higher levels of the SHM hierarchy, purely data-driven methods are found to be lacking. For higher-level SHM tasks, such as prognosis, or for furnishing a digital twin of a monitored structure, it is necessary to integrate the knowledge stemming from physics-based representations, relying on the underlying dynamics and mechanics principles. This paper discusses implementation of such a physics-enhanced approach to SHM.
Ultrasonic Solution for Structural Health Monitoring Using Embedded Non-Destructive Testing
A high number of actors of the space industry, including ESA (European Space Agency) and CNES (French national space agency), are engaged in a program aiming to develop future European reusable space launchers. In this context, PYTHEAS Technology develops an embedded Non-Destructive Testing (eNDT) solution aiming to ensure that strategic parts of the launcher are free of damage before a new launch. Structural damage can be detected, localized and characterized using a network of piezoelectric transmitters/receivers, embedded in the structure, which send and receive guided waves. The technique is based on a comparison between the baseline ultrasound signature of the structure and the deviation of a new ultrasound cartography from this reference. The system is tested experimentally on an aluminum honeycomb sandwich structure, specifically on a section of a payload adaptor. Detection, localization and characterization of different types of structural defects are carried out experimentally, and the performances of the system are discussed.
Debonding Detection in Aerospace Stiffened Composite Structures Using Ultrawideband Electromagnetic Tunneling
This paper proposes a novel approach based on ultrawideband electromagnetic tunneling for detection of debonding in composite structures. The idea behind this approach is to place two transducers at the opposite ends of a closed section stringer and analyse wave propagation in the hollow structure to detect damage occurrence and severity. A preliminary experimental campaign is carried out at laboratory level to optimize the setup and define damage detection metrics in view of aircraft fuselage monitoring. Results show that tunnelling approach can be used to assess the presence and severity of defect when air leak is present because wave field gets altered. In particular, microwaves in the frequency band from 4-8 GHz are mostly sensitive to the defect. In addition, damage metrics allow quantifying the damage occurrence and discerning noise in measurements from actual debonding.
Study of the Fiber Bragg’s Grating Sensor Sensibility Subjected to Harsh Environmental Conditions
This study investigates the performance of Fiber Bragg Grating sensors bonded with an cyanoacrylate adhesive to different substrate materials (titanium, aluminum, and carbon-fiber-reinforced polymers) for Structural Health Monitoring applications in the aeronautical industry. The results show that thermal aging alters the adhesive layer, ultimately modifying the FBG sensor's sensing ability. A finite element model is used to study the most influential factors on the bonding agent and FBG sensor durability.
Enhancing Interfacial Toughness in 3D-Printed Soft-Hard Interfaces by Fused Filament Fabrication
We proposed a facile strategy for architecting the interface morphology to create tougher and stronger interfaces in additively manufactured multi-material polymer composites in this study. A sutural interfacial morphology between two dissimilar polymer phases PLA (hard) and TPU (soft) is designed and fabricated by the fused filament fabrication technique. The interfacial toughness measurements by the double cantilever beam test reveal a linear correlation between the interfacial toughness and protrusion amplitude. The proposed interfacial architecture can result in up to a 16–18-fold increase in the interfacial toughness in comparison with the baseline interface.
The Application of Risk Minimization to the Selection of Fiber Optic Sensors for an Aerospace Structural Monitoring Application
This work proposes an optimal fiber optic sensor placement framework for structural health monitoring (SHM) applications. The framework is applied to an aircraft’s wing spar entirely made of composite materials. The damage of interest is debonding between laminates, which may cause local buckling that results in reduced structural load carrying capabilities. A high-fidelity finite element (FE) model is used as a synthetic data generator. The inputs to the model are loads and debonding damage parameters (size and location), and the outputs are uniaxial strain measurements and buckling eigenvalues. “Run time” surrogate models are created using different machine learning methods to overcome the high computational costs of each run of the physicsbased model. Then, Bayesian inference is used to estimate the damage parameters given strain measured at candidate sensor locations. These estimations are used to assess damage criticality, which is linked to buckling eigenvalues, and transformed into decisions. Bayesian optimization is used to select the candidates that minimize a utility function that considers the costs associated with making a certain decision plus the costs of acquiring and installing the SHM hardware (sensors, data acquisition system, etc.). The candidate with the lowest cost is selected. The resulting optimal sensor configuration is presented, consisting of the number of sensors to be deployed and their respective locations. The importance of defining an objective function that reflects the goal of the SHM system (e.g., maximizing the probability of detection, minimizing the probability of false alarms, or a balance of both) are also discussed.
Influence of Residual Stresses on the Dispersion Behavior of Guided Ultrasonic Waves in Fiber Metal Laminates
Fiber metal laminates (FML) are a promising material as they combine the high specific properties of fiber-reinforced polymers with the ductility and damage tolerance of metals. However, during manufacturing in-plane residual stresses arise in the single layers of the laminate. The stresses mainly originate from the different coefficients of thermal expansion of the two materials and lead to an inhomogeneous stress state in the thickness direction of the FML. From homogeneous materials, it is known that stress due to external loads affects the propagation behavior of guided ultrasonic waves (GUW) in thin structures. Nevertheless, the magnitude of this effect is mode and frequency dependent. In the case of FML, the question is whether the inherent residual stresses also influence the dispersion characteristics of propagating GUW. With modified cure cycles, different residual stress states are realized in FML specimens made of carbon fiber-reinforced polymer and steel. An established measurement procedure using laser scanning vibrometry and multi-frequency excitation is used to determine the dispersion diagrams for the manufactured FML specimens. After processing the data with a nonuniform 2d discrete Fourier transform, the phase velocities over the measured frequency for the fundamental modes are compared for the different residual stress states. The results indicate that the variation of the residual stresses in the investigated FML does not show any significant effect on the GUW propagation for the measured frequencies.
Towards Optimisation of Forced Heating and Cooling Automated Infrared Thermograph
Arguably the two most important characteristics (other than quality of inspection) of non-destructive evaluation (NDE) methods are speed and non-contact inspection. These attributes coupled with real-time, automated, online and/or in-situ methods give an insight into what is expected of future inspection systems. Thermographic imaging methods over the years have been found to be well suited to meet some of these requirements. In this work a novel real-time low-cost automated heating and cooling thermographic system tailored for use within the Industry 4.0 paradigm is evaluated. The focus of this work was to improve damage detection and imaging by the optimisation of heating and/or cooling of components after manufacturing or during maintenance cycles. By changing the heating and cooling patterns during real-time automated inspection the thermographic process can be optimised for damage detection. A carbon fibre reinforced plastic (CFRP) composite sample with flat bottom holes (FBH) was used to evaluate the effective of changing heating and cooling patterns. The system was assessed using three heating/cooling profiles: heating only (HO), cooling only (CO) and heating and cooling (HC). The results show that heating and cooling patterns can play an important role in improving the effectiveness of thermography.
Thermoplastic Induction Welded Joint Design for Structural Health Monitoring Damage Detectability
In the present work, the design of thermoplastic induced welded joints was carried out considering different aspects related to structural performance, manufacturability and structural health monitoring capability. Three lap-joints configurations were considered: two of them having welded plates with two different carbon fibers plies stacked layups and one obtained by welding carbon fibers fabric laminates. A numerical analysis was carried out by performing (i) structural simulations to assess the mechanical properties of the joints, (ii) induction welding process simulations to evaluate temperature distribution in the welded interface area and (iii) damage detection assessment by reproducing the response of a structural health monitoring sensor bonded in correspondence of the joint overlap area. Electromagnetic-based structural health monitoring techniques were employed to evaluate local variation of electromagnetic characteristics due to defect presence. The results obtained show that the design choices have a significant influence on the physical properties of the welded material such as the electrical conductivity anisotropy. The different induced currents distribution obtained during both the fabrication and damage inspection phase, affects respectively the manufacturing and detection capability. An experimental validation was carried out by mechanical tests under quasi-static loading for the case with the best tradeoff between structural performance, manufacturing feasibility and damage detection capability. The proposed approach show the importance of taking into account the requirement of damage detectability from an early stage of the design.
Model-Assisted Probability of Detection of Cracks Under Fastener Head
After the Aloha Airlines Flight 243 accident in 1988, the importance of inspecting for widespread fatigue damage was emphasized. Managing the risk of multi-site damage (MSD), where multiple flaws occur in a single structure, became a key factor in maintaining the structural safety of aircraft. It is essential to demonstrate the reliability of nondestructive inspection (NDI) when detecting structural damage due to MSD. Structures affected by MSD often require inspection without being disassembled due to various reasons, such as increased inspection time, inefficient manpower and material consumption, and increased probability of MSD due to human error. NDI reliability is strongly influenced by the shape of the test specimen, and the uncertainty about crack conditions after inspection is greater when inspection is performed on a structure without disassembly than when it is assembled. To estimate the reliability of NDI in structures without disassembly, a model-assisted probability of detection (MAPOD) experimental design was used, combining physical modeling and machine learning methods. The test specimens simulated flaws such as discharge machining notches and fatigue cracks, and the detection probability was estimated based on the size of the detection signal. This was used in risk assessment to determine the inspection interval for the structure.
Impact Monitoring of Large and Complex Structures Based on Transfer Learning
Aircraft structure impact monitoring is important to the safe operation of aircraft. However, aircraft structures are often structurally complex, increasing the uncertainty of the signal during transmission. Traditional impact monitoring methods need to obtain sufficient structural change signals through dense sensor arrays to obtain good monitoring results. But too many sensors can increase the cost of operating an aircraft. Therefore, this paper adopts sparse sensor array arrangement, proposes a two-step impact monitoring strategy from region to point location, and adopts deep learning and traditional methods to monitor impact events. Firstly, the test structure is divided into several regions of a certain size, and a model capable of accurate regional location is trained by convolutional neural network. In this process, in view of the large size of the aircraft structure and the difficulty in obtaining training data, the transfer learning strategy of model fine-tuning is adopted to transfer the trained feature knowledge of the source domain model to the target domain model, reducing the cost required for data acquisition and training model. Then in the second step, on the basis of accurate regional positioning, weighted centroid method is used to estimate the impact location.
Health Monitoring of a Lenticular Truss Bridge Using Wireless Strain Sensors and Finite Element Models: A Case Study
An increasing number of new and old structures are instrumented with some sort of structural health monitoring (SHM) systems, which include a variety of sensors driven or governed by dedicated hardware and software. This paper presents the monitoring of the Smithfield Street Bridge, one of the oldest and most iconic bridges in the city of Pittsburgh (Pennsylvania). The bridge was instrumented with wireless strain, displacement, and rotation sensors by an independent party not involved with the research presented here. In order to interpret the sensors' data, a finite element model was developed, and a static analysis was performed to quantify the deformation at certain members of the bridge under known loading conditions. The results of the finite element analysis were then compared with the results of a test in which a truck of known weight crossed the bridge multiple times. Finally, the data from the strain sensors relative to three-years of uninterrupted monitoring were processed and analyzed to identify eventual anomalies.
Scour Damage Detection of Bridge Piers Using the Vibration-Based Method: Numerical Study on a Steel-Concrete Bridge in France
The scour is one of the main reasons for bridge collapse. Soil removal around bridge foundations alters their embedment conditions and affects the stability of the structure. Therefore, scour detection is crucial to ensure the safety of bridges and prevent their failures. This paper presents a vibration-based analysis method for detecting scour in its early stages, using frequency values as a scour indicator. Scour is represented by the degradation of foundation stiffness. A robust expression is developed to calculate the variation of frequency as a function of stiffness variations. Different scour scenarios are numerically simulated on a steel-concrete bridge model, revealing that frequencies related to horizontally displaced modes are sensitive to scour, while the deck bending modes are insensitive. The SVD method used to solve the inverse problem has proven to be robust in estimating stiffness reduction based on frequency changes and identifying the location of scour.
Dynamic Bridge Monitoring with Remote Sensing Techniques
Dynamic monitoring of bridges is usually performed with mounted sensors on the structure. Typically used technologies are accelerometers, electric strain gauges or fibre bragg grating sensors. All of them have the drawback that access to the structures is needed. This usually results in bridge closures during installation or during the measurement campaign. Furthermore, every measurement position has to be equipped with a separate sensor. We explore an alternative approach where the dynamic bridge behavior is determined with remote sensing techniques. Profile based terrestrial laser scanning as well as interferometric radar measurements enable the contactless measurement of bridge profiles with measurement rates of up to several hundred Hertz. Furthermore, dynamic 3D displacements can be determined with robotic total stations and GNSS receivers. This article thus discusses the state-of-the art of dynamic remote sensing techniques and in addition investigates the spatial and frequency resolution in real world examples.
Bridge Frequencies Identification from the Dynamic Response of a Passing Vehicle
Based on the coupling nature of a vehicle moving on a bridge, Yang et al [1] proposed an indirect bridge frequency approach where the bridge response can be recorded by the passing vehicle. Then, the bridge frequencies can be obtained from the dynamic response of the moving test vehicle. Following this concept, in this study, an experimental setup was designed for a test vehicle moving on a bridge to measure the bridge frequency. From the present experimental results, the indirect bridge inspection method is feasible for monitoring the dynamic characteristics of a bridge.
Establishment and Initial In-Site Testing for the Structural Healthy Monitoring System of the Kinmen Bridge in Taiwan
The newly-constructed Kinmen Bridge is the first cross-sea bridge for the daily commuting and medical support issues between Kinmen Island and Leiyu Island (socalled Little Kinmen Island) in Taiwan. In order to well understand the initial state and following operation conditions, a tailored-made structural healthy monitoring (SHM) system is suggested and established for the bridge. This article, in detail, describes the planning and installation of this SHM system, including cable tension force, structural behavior, and environmental monitoring, as shown in Figure 1. Subsequently, a series of onset structural condition tests on the bridge are triggered as the force inspection of cable tension, ambient testing, loading testing, and analysis of characteristics during both static and dynamic states. The experimental outcomes effectively optimize and calibrate the relative parameters and boundary conditions in the structural model of the bridge. It is expected that the following scenario simulation could provide predictions on pretension loss, temperature effect, overloading, and earthquake effect on this bridge in future. Moreover, the several critical thresholds in the calibrated numerical model could be settled, and contingent alarms will be in-time announced to the bridge management unit. This joint structural analysis and SHM-based in-situ testing could achieve the aim of real-time monitoring and diagnosis for the Kinmen Bridge.
Structural Utilization Prediction for the Health Monitoring of Tunnel Linings by Means of an Artificial Neural Network Ensemble
The continuous expansion of underground structure networks and the necessity of guaranteeing the safety and functionality of the already built tunnels pose the necessity of developing techniques and methods to efficiently achieve these aims. Normally, measured data of different nature are available for tunnels due to sensors installed in the lining. However, adequate procedures have to be implemented to obtain valuable information out of the monitored data. A new method has been developed to achieve a real-time assessment of the stress state in segmental tunnel lining, given specific measured quantities as input. The method is based on the combination of finite element (FE) analyses and feedforward neural networks, which permits to exploit the advantages of both physics-based simulations, representative of the structure considered, and the predictive capabilities of machine learning tools. The FE model of the tunnel lining plays an important role for the reconstruction of the missing quantities, which are not available from monitoring campaigns, but which can be generated by numerical analyses. A Monte Carlo sampling procedure is performed for the definition of multiple sets of the input parameters used in the FE model for the generation of the training data of the metamodels. An ensemble of neural networks is created and assembled into a framework, which is validated against a full scale test where its predictive performances are investigated.
A Comparison of Structural Similarity Metrics within Population-Based Structural Health Monitoring
Population-based Structural Health Monitoring (PBSHM) aims to gain additional insights into the health of a structure when using data available across a population of similar structures, as compared to the insight available when using only data from a single structure. Before knowledge can be transferred across structures, the similarity between structures (or substructures), within the population must be established. As a result of recent developments within PBSHM, there are now several methodologies for the comparison of structures, including Graph Matching Networks (GMNs) and the Jaccard index. The recent work on GMNs has highlighted how different methodologies may be required for different structure types or populations. This paper looks at these aforementioned algorithms and evaluates the performance of each algorithm against different toy and real-world datasets and determines which algorithms have the potential for being used within which scenarios.
AI-Driven Structural Health Monitoring for the Tallest Curved Concrete Skyscraper
With the rapid increase in the construction of tall buildings around the world, ensuring their safety and structural integrity has become of paramount importance. This paper focuses on the Al-Hamra Tower, a remarkable skyscraper standing at an impressive height of 412.6 meters (1,353’-6”) in Kuwait City in Kuwait, and explores the role of Structural Health Monitoring (SHM) as a powerful tool for assessing building conditions, detecting damage following extreme events, and optimizing cost-saving strategies. Among the various factors that can lead to building failures, extreme events such as earthquakes in the Gulf Region pose the greatest threat. If damage conditions are not promptly identified, they can leave the structure vulnerable to further deterioration. Early diagnosis of damage is not only crucial for the safety of occupants but also proves to be cost-effective, as repairing minor damage is generally more affordable than addressing major structural failures. This paper presents an advanced AI-powered SHM system specifically developed for the Al-Hamra Tower. The study delves into the implementation of a dense array of instrumentation strategically placed throughout the tower to capture and measure its structural response under different loadings, including wind, seismic activity, and thermal effects. The SHM system incorporates an automated triggering mechanism that responds to predefined events, such as earthquakes, and promptly sends text and email notifications to alert relevant authorities. By highlighting the case study of the Al-Hamra Tower, this research underscores the critical importance of SHM for tall and critical buildings, offering invaluable insights into the design and implementation of effective SHM systems for future skyscrapers. Keywords: AI, SHM, machine-learning, earthquake, tall-building, multi-sensing, big-data, analytics, early-warning, seismic-design, neural-network
Monitoring the Deformation Pattern of an Instrumented Concrete Arch Dam
Structural Health Monitoring (SHM) and prognosis could play a crucial role in the life cycle of a critical infrastructure such as a large dam. Such monitoring practices serve multiple functions and are essential components in ensuring the safety and effective maintenance of such structures. By providing insights into a structure's behaviour, structural health monitoring assists in calibrating numerical models and complements visual inspections. This process facilitates informed decision-making, aids in planning maintenance and rehabilitation schedules, and ensures the desired safety of the structure throughout its lifespan. This paper presents a case study of a double curvature large thin concrete arch dam that is currently experiencing slow irreversible upstream movement of the central dam crest. The dam is wellinstrumented, with instrumentation falling into categories such as hydrometrological, geotechnical, geodetic, and seismic monitoring. The primary objective of this paper is to validate and establish a level of confidence in numerical modelling by comparing the displacement of an instrumented concrete arch dam with the with that calculated from its finite element method (FEM) model. To achieve this, a sequential thermo-mechanical analysis was performed on a 3D model of the dam foundation reservoir system. Additionally, the study utilizes a well-known statistical model called Hydrostatic-Season-Time (HST) model, based on multi-linear regression analysis, to predict the dam's future displacement using the relevant data from the sensors placed in the dam. The findings indicate a good correlation between the observed dam displacement based on instrumentation data with that calculated from the Finite Element Analysis (FEA) considering the presence of vertical joints. Moreover, the HST model proves to be a suitable choice as it closely matches the observed data and demonstrates good predictive capabilities for future dam behaviour and helps isolate the effects of different parameters on the dam displacement.
Structural Health Monitoring and Real-Time System Identification Analysis of the World’s Tallest Sculptured Skyscraper
With the rapid increase in the construction of tall buildings around the world, ensuring their safety and structural integrity has become of paramount importance. This paper focuses on the Al-Hamra Tower, a remarkable skyscraper standing at an impressive height of 412.6 meters (1,353’-6”) in Kuwait City in Kuwait, and explores the role of Structural Health Monitoring (SHM) as a powerful tool for assessing building conditions, detecting damage following extreme events, and optimizing cost-saving strategies. Among the various factors that can lead to building failures, extreme events such as earthquakes in the Gulf Region pose the greatest threat. If damage conditions are not promptly identified, they can leave the structure vulnerable to further deterioration. Early diagnosis of damage is not only crucial for the safety of occupants but also proves to be cost-effective, as repairing minor damage is generally more affordable than addressing major structural failures. This paper presents an advanced AI-powered SHM system specifically developed for the Al-Hamra Tower. The study delves into the implementation of a dense array of instrumentation strategically placed throughout the tower to capture and measure its structural response under different loadings, including wind, seismic activity, and thermal effects. The SHM system incorporates an automated triggering mechanism that responds to predefined events, such as earthquakes, and promptly sends text and email notifications to alert relevant authorities. By highlighting the case study of the Al-Hamra Tower, this research underscores the critical importance of SHM for tall and critical buildings, offering invaluable insights into the design and implementation of effective SHM systems for future skyscrapers.
Non-Destructive Testing and Evaluation of Timber and Steel H-Piles
Timber and steel H-piles have been used in the construction of many aging locks, dams, levees, and other structures in the United States. Failure of these piles may adversely affect the structural integrity, and potentially cause damage and/or catastrophic structural failure. This study aims at utilizing state-of-the-art nondestructive testing (NDT) techniques to assess the condition of steel and timber piles. Two pairs of timber piles and steel H-piles were prepared and nondestructively tested in the laboratory. The test parameters were the two types of piles and pre-planned defect sizes/locations. Four NDT methods were investigated including bending wave (BW), dispersive wave (DW), combinations of dispersive-bending wave (DW/BW), and parallel seismic (PS). Wavelet transform, an advanced signal processing method, was used to post-process the NDT data. The method known as “peak identification (PI)” was used to detect the pile tip in both BW and DW/BW tests. The results reveal that the DW/BW accurately detected the pre-planned defects for timber piles. For steel piles, the PI method could only detect deeper defects in the DW/BW tests. The reflected signals from shallower defects had a relatively high signal-to-noise ratio and were hard to interpret. The wavelet transform approach was successfully used to detect both the shallow and deep defects in all the piles. The PS tests were proved to accurately and effectively capture all the pre-planned defects in both the timber and steel H-piles.
Crack Monitoring of Lock Infrastructure Using Strain Sensors
Inland navigation systems facilitate barge traffic, the most efficient tonnage per fuel method of transportation. Miter gates allow barges to bypass navigational dams. The Dalles downstream miter gate has experienced numerous instances of cracking, threatening its continued operation and shipping on the Columbia River. The earliest instance of cracking led to the installation of a sensor system to ensure continued operation. The most recent serious cracking has resulted in serious efforts to track the cracking by moving some existing sensors and leveraging on-site underwater ROV cameras. A simple method is proposed to choose strain gage locations sensitive to cracks. A corresponding structural health monitoring algorithm to monitor the crack with strain gage data would benefit from supervised learning techniques. Thus, the existing strain gage system is leveraged to validate the finite element model, which in turn enables supervised learning techniques. It is found to be necessary to update the finite element model for acceptable validation.
Investigation of Retroreflective Sheeting Materials Response to Induced Strain While Mounted to Common Civil Substrates for Use as Passive Strain Sensors for Structural Health Monitoring
Retroreflective sheeting materials (RRSM) are used for various applications in engineering, although primarily for traffic signs and roadway markings. There are several ASTM standard types of RRSM that have required values of retroreflection to ensure safe usage. Retroreflectivity (RR) is the portion of light returned to the light source measured in candelas per lux per square meter, as measured using a retroreflectometer. As load is applied to RRSM, the retroreflection changes, and many have a reasonably linear relationship to the material’s strain (ε), thus, opening the possibility for using RRSM as a passive strain sensor for structural health monitoring that is low cost, practical, and innovative. Material sensitivity (retroreflectivity divided by strain) is the most important factor when evaluating various standard RRSM types for potential use as passive strain sensors. Previous work has showed that a manufacturer, designated “B”’s standard Type VIII (designated B-TVIII) material has the highest retroreflective sensitivity to induced strain, making it the best candidate for passive sensing. Other materials are also candidates, such as manufacturer “A”’s Type XI white (A-TXIW). To continue to evaluate RRSM’s potential to be used as a sensor, an understanding of the material’s behavior while adhered to common civil substrates, such as concrete and steel, is necessary. RRSM was mounted to steel and cyclically loaded in tension to examine the RR versus microstrain (μm) relationship, assess mounting procedures and compare results to previous tension tests of the bare materials. Initial results show that the RR-μm relationship of the material tested alone in tension differs from the same relationship when it is adhered to a steel specimen. Several factors were considered, such as surface preparation, RRSM length and adhesion materials to understand why the relationship differs. Through testing, it became apparent that temperature may have an effect on the results and temperature vs RR of B-TVIII and A-TXIW were evaluated and it was determined that the retroreflectivity of the material is lower at lower temperatures.
Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) Deployment for Community Driven Decision Making
There have been several advances in Structural Health Monitoring (SHM) throughout the last two decades. Among these advances is that sensors and data acquisition have become smaller in size while wireless technologies have been making wireless communication and data accessing easier. These advances create cost effective sensing solutions for communities where flooding and wildfires put their members and infrastructure at risk. Therefore, with higher community involvement in understanding and utilizing new sensing technologies, there is more to be gained in preparing for and mitigating the effects of natural hazards. Low-cost easily deployable sensors will make sensor technology more popular and easier for communities to utilize and give them the ability to make decisions during natural hazards. LEWIS, a Low-cost Efficient Wireless Intelligent Sensor platform, is created by the Smart Management of Infrastructure Laboratory (SMILab) at the University of New Mexico (UNM) at Albuquerque for such a purpose: to give communities the ability to create innovative monitoring solutions, including combating climate change. This paper briefly discusses the LEWIS platform, their use for communities to combat natural hazards to make quick decisions to improve public safety, training and education components, and community (from student to industry professional) engagement efforts.
Development of Nanocarbon Black-enabled Self-strain Sensing Ultra-high-performance Cementitious Composites
In this study, nanocarbon black (nCB) is employed to make the matrix of UHPC conductive. The nCB-UHPC composite is investigated for its fresh and hardened mechanical properties, piezoresistive behavior and microstructure. Piezoresistive behavior and strain sensitivity are measured by fractional change in electrical resistivity (FCR) with applied stress/strain. The nCB-UHPC composite exhibits adequately good workability (slump spread ≥ 170 mm), compressive strength of more than 144 MPa and an outstanding sensitivity (gauge factor > 130) achieved with 1.5% nCB. Additionally, it has shown a repeatable, highly sensitive compressive strain response up to 40 MPa. The proposed high-sensitivity nCB-UHPC composite demonstrates significant potential in becoming a field-deployable solution for the health monitoring of concrete infrastructure.
An Electrical Infrastructure Management Model for Long-Term Maintenance and Hazard Mitigation
Electrical lines are critical infrastructure components. Line failures can cause catastrophic wildfires resulting in immeasurable damage to life and property. This paper discusses the implementation of an electrical infrastructure management model that tracks component degradation over time and incorporates inspection and monitoring data to update the model; the model then identifies components with the highest risk of failure to direct maintenance operations and estimate long-term line performance. The capacity of the structures is represented through fragility curves based on first-principles modeling and industry standards. Threat models, such as metal corrosion and wood decay, estimate how the fragility curve will evolve with time, based on environmental data and the age of the structure. Data-driven approaches are used to incorporate field data and historical performance into the model. Bayesian updating is used to adjust the fragility curves based on historical wind and outage data. Similarly, observed deterioration from field condition inspections are applied to further modify the model. Using a site-specific hazard model, an annualized probability of failure is estimated for each structure on an electrical line. First, an overview of the framework is provided, with a specific focus on wind hazards. To illustrate the application of the model and illustrate the challenges encountered in implementation, an example case study is discussed, exploring how the predicted probabilities of failure can be impacted by the choice of wind hazard model. This choice can result in geographic bias towards both under- and over-predicting probability of failure, and therefore can result in misdirection of maintenance resources. Future planned improvements are discussed.
Evaluating Post-Tensioned Trunnion Girders: A Comparative Study of Scale Model Tests and Numerical Analyses
Water flow in dams is commonly regulated using Tainter or miter gates, which are extensively employed in the navigation network of locks and dams managed by the United States Army Corps of Engineers (USACE). Tainter gates are well known for their effectiveness in managing the flow of water through dam spillways. Post-tensioned Tainter gate anchorages are widely utilized in numerous dams across the nation, particularly within the Mississippi Valley Division (MVD), the Great Lakes and Rivers Division (LRD), the Southwestern Division (SWD), and the Northwestern Division (NWD). Between 2010 and 2017, ten dams underwent testing, revealing that eight of them had failed rods. Out of the 5,371 greased trunnion anchor rods tested, 22 were found to be broken, and 6 had slipped gripping hardware. While the overall failure rate might appear low, a detailed analysis shows that individual anchorage failure rates ranged from 2 percent to 29 percent, potentially affecting anchorage capacity and performance. Apart from the trunnion rods that failed due to breaking or slipped connections, 278 rods (202 on Markland Dam and 76 on Greenup Dam) exhibited significant cantilever bending or corrosion, conditions that can contribute to anchor rod failure. This study aims to establish experimental and numerical methods for posttensioned anchorages with different rod configurations. Laboratory tests on scaled anchorages, featuring a concrete trunnion girder with nine high-strength post-tensioning rods, were conducted. Finite element (FE) analyses were validated using experimental data, replicating various trunnion rod failure scenarios. The FE results accurately predicted load changes on each rod under different loads and de-tensioning configurations. The findings from this research provide valuable insights into anchor rod failure rates, which can assist district engineers in assessing the current condition of anchor rods and planning proactive maintenance and remediation strategies.
Abnormality Detection Algorithm of Horizontal Displacement Monitoring Data During Foundation Pit Excavation Based on Temporal-spatial Characteristics
Horizontal displacement of the enclosure structure or soil is one of the key indicators to evaluate the safety of deep foundation pit excavation. However, the monitoring data of horizontal displacement will have short- or long-term abnormal fluctuations due to the interference of various non-structural factors, which would directly affect the assessment and identification of the risk of deep foundation pit excavation. In order to avoid missing and misjudging the true large deformation of the foundation pit and consider the requirements for the accuracy and timeliness of the monitoring data, an abnormal data identification and correction algorithm was proposed in this paper considering the temporal-spatial characteristics of the horizontal displacement monitoring data. The algorithm builds a spatial distribution matrix according to the deployment position relationship of multiple sensors, records the historical measured values of sensors in a period of time, and calculates the estimated values of the measured values of sensors at the next time based on multidimensional Kalman filtering. After obtaining the latest horizontal displacement monitoring value at the current time, calculate the difference between the measured value and the estimated value based on the spatial matrix to correct the data, mark the sensor with the difference greater than the adaptive threshold, and finally update the historical measured value, adaptive threshold, Kalman filter parameters, etc. in the window, waiting for the input of the monitoring data at the next time. The abnormal data identification and correction algorithm described in this paper was successfully applied in a deep foundation pit excavation project in Shanghai. The anomaly identification and correction of single sensor data and the horizontal displacement curve were both given. In general, this method can simultaneously discriminate and correct the data of multiple sensors in real time, effectively eliminate the abnormal fluctuation of monitoring data, and has good engineering application value.
A Model to Monitor Cracked Reinforced Concrete Structures Using Coda Waves
Monitoring of reinforced concrete structures is of paramount importance in ensuring structural health and reliability. To this end, this paper formulates a novel model for evaluating cracked reinforced concrete structures using ultrasonic coda waves. The proposed εsm-dv/v model combines the well-established σs-εsm relationship with the highly sensitive ultrasonic coda waves, allowing the prediction of strains in the reinforcement independent of geometry, material, and reinforcement ratio. After derivation, the proposed model is validated by experiments on reinforced concrete beams with different dimensions and concrete properties. The results show that the model accurately predicts strains in the reinforcement and thus offers great potential for practical applications in monitoring of reinforced concrete structures.
Experimental Characterization and Computer Vision-Assisted Detection of Pitting Corrosion on Stainless Steel Structural Members
Pitting corrosion is a prevalent form of corrosive damage that can weaken, damage, and initiate failure in corrosion-resistant metallic materials. For instance, 304 stainless steel is commonly utilized in various structures (e.g., miter gates, heat exchangers, and storage tanks), but is prone to failure through pitting corrosion and stress corrosion cracking under mechanical loading, regardless of its high corrosion resistance. The pit growth typically follows a sigmoidal trend with an initial high growth rate during nucleation, followed by an eventual saturation limit, which will ultimately lead to material failure. In this study, to better understand the pitting corrosion damage development, controlled corrosion experiments were conducted to generate pits on 304 stainless steel specimens with and without mechanical loading. The pit development over time was characterized using a high-resolution laser scanner. In addition, to achieve scalable and automatic assessment of pitting corrosion conditions, a convolutional neural network-based computer vision algorithm was adopted and implemented to identify the existence of pitting damage.
Validation of Data for Use in Civil Infrastructure Big Data Applications
Big data applications are becoming more popular across many different fields and civil engineering is no different. The benefits of big data lie in its potential to provide valuable insights into various large datasets. Big data applications can identify patterns and trends that were previously unknown, which can help them make informed decisions and develop effective strategies. In the case of civil engineering, this could be taking large datasets that have been produced in relation to pieces of infrastructure and using them to create more efficient management strategies. One of the issues with using big data is that if the input dataset is flawed then the output resulting from any big data analysis will be compromised. Therefore, data validation is used, which is the process of ensuring that data is accurate, complete, and consistent. The consequence of not undertaking data validation may be Inaccurate or inconsistent data which can lead to incorrect insights and decisions in big data applications. This paper explores the necessity of validating data before it is used in a big data application. It outlines some of the different methods used for validating data and provides an overview of the potential issues that may arise from data validation errors. A case study is then presented showing the process of validation on data collected from four bridges and provides recommendations for implementing data validation as part of a larger big data workflow. The results of the case study show that validation of data is an important step in the big data process both for confidence in the outputs and to make big data applications more useful and more common in the civil engineering field. The discussion and presented case study in this paper highlight the necessity of validating data. It has shown some of the potential issues that may arise from not undertaking data validation.
Application of Neural Network Entropy Algorithm and Convolution Neural Network for Structural Health Monitoring
This study combines Neural Network Entropy (NNetEn) and Convolutional Neural Network (CNN) to develop a practical structural health monitoring system. In order to verify the feasibility of the system, the failure experiment of a seven-story steel frame has been carried out with a numerical model of the same structural characteristics as the steel frame. The state space method is used to simulate the sixteen failure modes on the steel frame, where the acceleration signals of each floor at the time of failure are analyzed by neural network entropy. An entropy database is established based on the model to train the neural network model. To avoid the misjudgment and automatic interpretation of human factors, this study uses the visualized heatmap to quantify the change of entropy value, and the convolutional neural network analysis is selected for image processing. By converting the entropy value into image data, not only the number of parameters in the model can be reduced, but its operation speed can be improved. During the training process, the neural network model extracts and learns the damage features in the entropy value. After the training is completed, the model can allocate the damage area of the structure by identifying the damage features of the input data. Finally, through the verification of 16 failure cases simulated on the seven-story steel frame of the National Center for Research on Earthquake Engineering (NCREE), the performance of the proposed SHM system is evaluated by both numerical simulation and experimental verification with confusion matrix. The SHM system proposed in this study combines the emerging entropy analysis method with a neural network. The test results of the final verification have an accuracy rate of 93.13%.
Combining Ultrasonic and Electromagnetic Techniques for Early Detection of Corrosion Damage in Reinforced Concrete Structures
Corrosion of reinforcing bars has been established as a primary contributor to the deterioration of reinforced concrete (RC) structures. As corrosion products accumulate and expand, they generate tensile stresses within the concrete, ultimately leading to cracking and spalling of the concrete cover. Additionally, the reduction of cross-sectional area in reinforcing bars undermines the bonding strength of RC structures. To prolong the service life of RC structures, it is crucial to develop a robust monitoring system for early-stage corrosion damage assessment. Ultrasonic waves are sensitive to cracks and voids due to the large contrast in acoustic impedance between air and the background material, while electromagnetic waves are sensitive to metallic structures due to the substantial contrast in dielectric permittivity and conductivity. Therefore, this work studies the feasibility of combining ultrasonic and electromagnetic techniques for detecting early signs of corrosion damage, including cracks and reductions in the cross-sectional area of reinforcing bars. Ultrasonic coda wave interferometry method is used to monitor the growth of internal cracks in the reinforcing bar. A linear multiple-input-multipleoutput (MIMO) GPR array is employed to image reinforcing bars in the concrete. In the early stages of corrosion, rust and small cracks grow, leading to changes in the GPR image. The performance of the proposed ultrasonic and electromagnetic methods has been demonstrated by the accelerated corrosion monitoring test in the lab.
Strain and Acceleration-Based Finite Element Model Updating of a Concrete Highway Viaduct
This paper presents a finite element model updating (FEMU) study performed on a single span of a multi-span concrete highway viaduct, equipped with permanent monitoring and a bridge weigh-in-motion (B-WIM) system. The paper focuses on the comparison of updated variables from two different types of measurements: (1) longitudinal strains, induced by calibration vehicles and measured by sensors primarily used for B-WIM, and (2) natural frequencies of the superstructure, that were obtained from ambient and traffic-induced vibrations. In addition, the contribution of the concrete safety barriers to the superstructure stiffness is discussed.
Implementation of Decision Analysis on a Structural Health Monitoring System Applied to a Bridge Benchmark Study
Structural health monitoring (SHM) of bridges addresses the need for efficient and cost-effective monitoring of structures. The purpose of SHM is to detect structural damage and provide information related to maintenance, inspection, and repair. This information would then be supplied to asset managers in order to make improved maintenance decisions. This paper presents an investigation into the connection between SHM and decision-making via Bayesian decision theory and the value of information (VoI) obtained from SHM. The results and properties of an SHM damage classifier are used to update the probabilities of a tree-based decision model. Two analyses are included. Firstly, a sensitivity analysis of the value of information (VoI) of an SHM system for varying cost ratios is performed. The SHM system was applied to a benchmark study in which the dynamic behaviour of a steel bridge was measured for both undamaged and damaged structural conditions. Secondly, the relationship between the expected costs of the available actions and the size of the sensor network of the SHM system is obtained by selecting sub-sets from the complete measurement system utilized in the benchmark system. The applicability of SHM results for informing and updating damage probabilities is demonstrated.
Evaluation of Electro-Mechanical Impedance (EMI) for Structural Health Monitoring (SHM) of Post-Tensioned Ground Anchors
The US Army Corps of Engineers has demonstrated a need to monitor ground anchors within their large civil works portfolio in order to avoid structural damage to critical infrastructure. Ground anchors (ie, “tiebacks”) are used for securing loose soil or rock by placing a tendon (such as seven wire strand) into a predrilled hole, pulling it into tension against an anchor plate or block, then typically grouting it into place. A common failure mode of these ground anchors is tendon degradation due to corrosion resulting in tension loss. However, the tendons are difficult to monitor due to embedment in material limiting visual inspection to exposed anchor plates, wedges, and strand ends. Furthermore, retrofitting sensors onto existing anchor installations is limited to the exposed components. Therefore, instrumentation is desired to detect and quantify corrosion and/or tension loss of the embedded tendon as part of a comprehensive Structural Health Monitoring (SHM) plan. An effort to evaluate instrumentation to this end was undertaken by the authors. A process for inducing accelerated corrosion of cable sections using additively manufactured corrosion cells was developed in order to facilitate a more time-efficient evaluation of sensing modalities’ ability to detect corrosion induced material loss. Literature reveals that electro-mechanical impedance (EMI) using PZT (Lead zirconate titanate) patches (i.e. piezoelectric transducers) has been previously experimentally evaluated for both corrosion monitoring of steel components and cable tension respectively, although not concurrently. This paper presents an experimental methodology and laboratory results of corrosion detection and tension monitoring using PZT patches affixed to seven strand steel wire rope. Preliminary results from separate experiments indicate an ability to detect material loss due to corrosion and changes in tension using EMI.
Vehicle and Truss Bridge Interaction Adopting a Simplified 2D Model
Indirect structural health monitoring of bridges using vehicle-mounted sensors offers a promising approach for the continuous assessment of structures. Full-scale modelling of complex structures, however, is computationally intensive. In order to optimize the required computational expenses, a two-dimensional simplified numerical model whose response resembles the real structure is developed. Euler-Bernoulli frame elements are adopted for finite element structural modelling. The transitional Markov Chain Monte Carlo technique is then applied for Bayesian finite element model updating. The proposed approach proved to reach an accurate model for a real-world truss bridge in Japan. Vehicle bridge interaction elements are then developed based on the updated simplified 2D model. The generalized-α time integration scheme is used to overcome inherent numerical instabilities. Time-response of the vehicle is evaluated using the developed Vehicle-Bridge Interaction (VBI) framework to study the dynamic properties of the bridge. The proposed approach significantly reduces the computational efforts in extracting time history responses of the moving dynamic system over the bridge without sacrificing the prediction accuracy. Time history responses can be processed subsequently to identify damage(s) in the structure, if any.
Structural Health Monitoring of CFRP Propellers by Piezoelectric Line Sensors
Recently, marine propellers made of carbon fiber reinforced plastics (CFRP) have been put into practical use for merchant ships. CFRP have advantages of high specific stiffness/strength and vibration damping property, and these can reduce the weight and noise of propellers to increase the efficiency and comfort in operation. However, they are more susceptible to impact or cavitation that cause critical damage than conventional propellers made of a copper alloy. Therefore, it can improve the reliability of CFRP propellers to assess the structural integrity based on structural health monitoring (SHM). In this study, piezoelectric line sensors were embedded into intact and damaged blades, respectively, in CFRP propellers with which a leisure boat was equipped. We could successfully detect vibration deformation of the propellers during the boat was moving, and we found that the amplitudes of the measured signals in the damaged blades were larger than those in the intact ones. Consequently, we can say that the applicability of the embedded piezoelectric sensors to SHMsystem for marine propellers is shown and it will increase the reliability not only of the propellers but also of the ship operation in the sea.
Online Monitoring System for Short Stator Maglev Train
Maglev train is a new kind of transportation mode that uses magnetic force to levitate and propel the train along a track. It is becoming more popular especially in China, where several maglev lines are in operation or under construction. Unlike the traditional wheel-based train, maglev train is suspended above the track, with a desired gap of about 8 mm. Due to the small gap, any disturbance of the suspension system may cause failure of suspension. The sources of disturbance include irregularity of track, variation in load, variation in parameters of the train structure (such as stiffness and damping of air spring), unexpected coupling vibration of the train-track-bridge system, and large displacement of the track and bridge. An online monitoring system was designed and installed on a maglev train and the corresponding line to collect the necessary data for analyzing the condition of the train and line and the mechanism of dynamic interaction of the train-track-bridge system. The monitoring items for the maglev train include the acceleration of the maglev bogies and carriages, the suspension gap, and the current of electromagnets. The monitoring items for the line include the acceleration of the bridge and track in concrete straight section, concrete curve section, tunnel wall and steel turnout. They also include relative displacement between concrete bridge and track, and strain of the track. Piezoelectric accelerometers, laser displacement sensors, fiber Bragg grating sensors and electric eddy displacement sensors are used in the monitoring system. All sensors are connected to data acquisition equipment by signal cable. The online monitoring system detected some unexpected conditions of the train and line, such as over-limit irregularity of the track, vibration resonance between the train and track, and suspension failure of the train. The data collected when the train was operating at different speeds and passing through different sections of the line was analyzed. The online monitoring system helped to speed up the debugging process before the maglev line started its official operation.
An Innovative Approach of Vibration Testing of Concrete Structures Using Performance Based Evaluation Techniques
Many of the current state of the art structural health monitoring (SHM) techniques are reliant on ambient excitation to determine the modal behavior of a structure. Common sources of ambient excitation are wind, water flow through dams, traffic on bridges, or seismic activity. However, it is possible for none of these sources of ambient excitation to be available for a structure. For example, dams can have strict limits on how much water can be released over certain time periods and may not allow traffic on them. In these cases, it can be necessary to induce modal excitations from other sources such as mechanical shakers or a cold gas thruster (CGT). Mechanical shakers allow specific modes to be activated but are heavy and require space that may not be available on all structures. The U.S. Army Corp of Engineers (USACE) has many large concrete dams that require such excitation to obtain modal information. This information may then be used to directly obtain changes in fixity or to calibrate finite element models of the structure. To determine the limits of the CGT for characterizing a structure, a reinforced concrete test structure was subject to the CGT pulse force in several locations with 8 accelerometers recording the motion of the structure. Using results from this effort, a shock response spectrum can then be used to compare a model’s behavior to that of the structure itself and to validate or match a model. Additionally, knowledge of a structure’s dynamic behavior in both an intact state and a damaged state can directly identify a change in boundary conditions, indicating separation of monoliths or foundation sliding The reinforced concrete test structure was used to test the CGT, as well as to establish the methods used to match the finite element analysis (FEA) models to a physical test. A small CGT was used to accelerate the structure in several configurations, which included changing the location and direction of the load. The Shock Response Spectrum (SRS) is used to compare the response of the structure to both different loads and to the modeled behavior of an FEA model built in LS-Dyna. The material attributes of the concrete and soil beneath the slab are unknown, but the LS-Dyna model matches closely to the physical tests. Peaks in the SRS can indicate modal frequencies, while double peaks in a symmetric structure can indicate some asymmetry, either from additional mass, or more likely, from reductions in stiffness. The addition of mass or reduction in stiffness may be added to the model to determine exactly how or where damage has occurred. This will discuss how the models are matched to the structure using the shock response spectrum and how damage may be indicated by shifts in the shock response spectrum, as well as the appropriate structures for this application.
Diagnosis of Concurrent Sensor Faults in Structural Health Monitoring Systems
Sensor faults in structural health monitoring (SHM) systems may occur due to aging, exposure to harsh weather conditions, manufacturing defects in hardware components, damage during installation or operation, and issues with data transmission. If undetected, sensors faults may result in inaccurate or incomplete sensor readings, which may significantly impact the accuracy, reliability, and performance of SHM systems. As a result, fault diagnosis in SHM systems may help improve the accuracy, reliability, and performance of SHM systems. However, most fault diagnosis approaches for SHM only consider single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems, where sensor faults may occur concurrently in multiple sensors. To extend fault diagnosis in SHM towards concurrent sensor faults in multiple sensors, this paper presents an adaptive fault diagnosis approach based on analytical redundancy. The approach encompasses four steps, (i) initialization (ii) fault detection, (iii) fault isolation and (iv) fault accommodation, using correlated data from multiple sensors of an SHM system. The proposed fault diagnosis approach is validated using data recorded using a real-world SHM system. The results show the high accuracy, reliability, and performance of the proposed approach in detecting concurrent sensor faults in real-world SHM systems.
An Influence of Temperature on Additive Manufactured Composite with Embedded Fiber Bragg Grating Sensor
Additive manufacturing (AM) is a name for techniques applied to constructing threedimensional (3D) objects in a layer-by-layer process. AM methods can be applied for creating elements from fiber (glass/ carbon) reinforced polymers. During the process, fiber Bragg grating (FBG) sensors can be embedded into the element structure for the purpose of structural health monitoring (SHM) system development. Such an approach combines in one, advantages of AM (limited waste, elements with complex shape) and SHM system (safety, information about real loading conditions). Such a method can be applied for the manufacturing of different elements applied in many branches of industry, e.g. marine or civil engineering. The goal of the paper is to analyze the temperature influence on AM fiber (glass/ carbon) reinforced polymer structures with embedded FBG sensors. The analyzes will be focused on the influence of FBG sensors on material durability. Additionally, the influence of elevated temperatures on the finished AM elements will be investigated.
Assessment of Corrosion Damage in Steel Samples Using Electro-Magnetic Acoustic Measurements
Ultrasonic thickness resonance and pulse propagation speed can be effectively used to find the thickness of thin-walled structures present in the skin of aircraft, fluid handling equipment such as pipes and in fluid storage tanks. Electromagnetic Acoustic Transducers (EMATs) yield several advantages over more traditional piezoelectric based transducers, namely that they can be used in a non-contact fashion allowing measurement regardless of surface condition or temperature. The main disadvantage of EMATs is their low efficiency. Therefore, resonance techniques, in which most of the energy is concentrated near structural resonance frequencies, are preferable to boost efficiency of electro-magnetic acoustic measurements. Thickness resonance is a technique commonly used to measure small changes in structural thickness, by integration of a ringdown curve in a wide time-domain window at different excitation frequencies. The Pulse Propagation Speed technique relies on the fact that ultrasonic waves travel faster in ticker plates in comparison to thinner plates at the same frequency. These techniques were applied to 316L stainless steel thin plates. Samples consisted of different thickness rolled material, machined samples, and samples artificially corroded with both uniform corrosion as well as pitting corrosion. These samples were used to determine the effect of thickness reduction and corrosion on the resonant peaks and lamb wave velocity. EMAT liftoff distance was also studied to quantify its effect on the amplitude, spread, and frequency of resonant frequencies and signal transition. Detectability of corrosion using EMATs was explored and quantified. Recommendations for use of EMATs for assessing corrosion damage were presented.
Wayside Acoustic Fault Diagnosis of Train Wheelset Bearing Based on Improved Frequency Sparsity Bayesian Learning
Important train component like wheelset bearing endure severe mechanical loads and alternating stresses for an extended period when the train is in operation. The cracks due to bearing fatigue are often difficult to detect in time, which is the biggest hidden problem for train safety. The wayside acoustic detection of train bearing faults plays an important role in maintaining the safe operation of trains. However, the acoustic signals collected by the wayside microphones are usually complex, mixed with other irrelevant signals with strong background noise when in the condition monitoring of wheelset bearings. Moreover, when the train passes through the detection area at a certain speed, the microphone picks up the acoustic signal for a short time, which makes it difficult to obtain accurate fault signal characteristics. Trackside acoustics often uses microphone arrays to overcome the problem of short sampling time. To diagnose wheelset bearing faults more effectively with fewer microphones, this paper proposes a wayside acoustic fault diagnosis method based on improved sparsity Bayesian learning. Advanced sparse representation (SR)-based approaches usually include two primary stages: recovery of fault pulses in the time domain and frequency transformation of the estimated signal envelope. However, any inaccurate signal recovery in the time domain can introduce the problem of error accumulation in the subsequent frequency transform, which suffers from the disadvantage of low resolution, especially for short time sampled acoustic signal. Taking the limitations into consideration, a new algorithm has been developed by combining the group sparsity and periodic structure of fault pulses with variational Bayesian inference (VBI) technology. This advanced method effectively extracts the fault pulse signal, even in the presence of high noise levels, which leads to a significant enhancement in the fault detection performance. With the aid of this method, in conjunction with the Doppler distortion preprocessing correction algorithms, the proposed approach has been successfully applied to the diagnosis of faults in train trackside acoustic wheelset bearings. The experimental results show that the proposed method can achieve good performance in fault diagnosis compared with conventional analysis method, making it more effective to monitor wheelset bearing faults.
Diagnostics of Anomaly Steam Turbine Behavior in Terms of Remote SHM and Cloud Computing
Monitoring of structural health is the important task in terms of ensuring the reliability of the operated technology. It is important especially in case of the steam turbine operation where the unplanned outage is associated with the high financial losses. The conventional approach for the evaluation of the normal turbine operation is based on precise specification of the trigger limits for each of the measured signals made by the technical expert. The abnormal operation is then determined when one of the signals is outside of the limits. However, it is a non-trivial task to specify trigger limits for all measured signals and sometimes it is even not practicable. The method described in this paper is based on the automatic detection of anomalies in the turbine behavior without the need to precisely specify the trigger limits manually. The approach is based on two steps. The first is to find the behavior of the turbine that is related to the normal turbine operation. The signal trigger limits are evaluated automatically with probabilistic assessment. The second step is to investigate the actual operation state using the signal measurements. Then if the actual behavior of the turbine is out of the boundary of the normal turbine operation, the anomaly is detected. The described method was validated using the measurement data acquired in operation of a steam turbine with a nominal power of hundreds MW. As an example of the method application, the paper shows the detection of an anomaly, which was subsequently identified as a contact between the stator and the rotor turbine part. This contact is potentially dangerous because it can change the structure of the machine. The described method is being integrated into the remote monitoring system that is based on cloud computing and being developed by the authors of this paper. The remote sensing of the turbine operation is nowadays the key to reduce costs in terms of maintaining the installed system and frequent visits of technical personal support to collect the data. The architecture of the monitoring system itself is described in the paper. The system is important in terms of providing an early warning in case of an unexpected behavior of the turbine. This provides the maintainability and reliability of the operated technology. Nowadays more than 30 operated turbines are part of this monitoring system worldwide.
Wind Turbines with Optimized Productivity through Fleet Monitoring without Additional Sensor Technology
The paper is a brief presentation of a recently started research project (WEAproduktiv) on optimizing the electricity production of wind turbines (WT) by means of population or fleet monitoring. The project is not mainly about Structural Health Monitoring (SHM), but about finding the causes of suboptimal power production. Nevertheless, damages, manufacturing defects or inaccuracies as well as suboptimal control, etc. are some of the reasons, which can lead to a loss of power production. We assume that e.g. causes like damages, manufacturing defects or a poorly functioning of the control system of a WT are reflected to some extent in the vibration behavior of the plant. As shown below, there exist many causes for production losses. They can be most easily detected by means of population monitoring. The objective is to identify “suspect” plants in wind parks and the causes of their power production losses using only Supervisory Control and Data Acquisition (SCADA). But first, the relationships between SCADA, vibration data and power productivity must be better understood. To achieve this, a numerical (digital twin) and a data driven model (physical twin) of a socalled fleet leader are included in our calculations. In a second step, the digital twin and the data driven model will be extended to understand and to model the population of wind power plants in different wind parks. The decision whether the intended population monitoring is possible with SCADA data alone and therefore the vibration data can be omitted, will be made towards the end of the research project. This depends on the meaningfulness of the generated models.
Metal-Organic Frameworks-Based Sensors for Detection of Toxic Environmental Contaminants
Continuous industrialization has led to the release of many heavy-metal pollutants from industrial wastes into the natural environment. In the recent decade, there is an increasing demand for accurate environmental pollution monitoring and control requires new sensing techniques with high sensitivity, selectivity, and reliability. Metal−organic frameworks (MOFs) have received considerable attention for environmental contaminant detection including anions, heavy metal ions, volatile organic compounds, and toxic gases because of their high surface area, porosity, large pore volumes, tunable structures, and open metal sites. A Fe3+ ion-selective electrochemical sensor was fabricated by depositing the synthesized Cu(TBC)-MOF on a glassy carbon electrode (GCE) and the electrochemical method was investigated in detail in an aqueous medium. The proposed Fe3+ ion sensor exhibited a linear relationship for current vs. Fe3+ ion concentration in the concentration range of 0.001 M to 100 M. The limit of detection value was found to be 0.52 μM in aqueous media which is lower than the acceptable value of iron in drinking water (0.3 mg/L). The presence of other ions such as Na+, K+, Mg2+, Pb2+, Zn2+, Cd2+, Cl-, NO -, and SO 2- 3 4 did not interfere with the detection of Fe3+ ions.
Structural Health Monitoring Application on Chemical Mill Line Cracking
The FAA’s Structural Health Monitoring (SHM) research program is integral in the FAA’s regulatory policy and guidance development for the implementation of SHM in civil aircraft. It also assists the FAA’s Emerging Technology Research program in numerous test scenarios. Various SHM technologies get real structure testing and provide the FAA with inspection results. Further, the SHM research team also participates in the SAE Aerospace Industry Steering Committee (AISC) for SHM. This paper will provide a brief history of the SHM research program and its assistance in FAA certification of SHM use. It will provide results from one of the Emerging Technology test programs at the FAA William J Hughes Technical Center focused on the evaluation of two SHM technologies and their ability to detect chemical mill line cracking on a full-scale test panel.
Variational Mode Spectrum Estimation for Wire Rope Defect Inspection Signals Analysis and Quantitative Evaluation
Wire rope is one of the most significant parts in guaranteeing the safety and health of infrastructures and practical structures. However, the defect frequently occurs when the wire rope is operating under sophisticated conditions, which makes the servicing wire rope and its related structures full of risk. Although many nondestructive testing techniques have been considered to solve the defect inspection and structural health monitoring (SHM) problems for wire rope, the inspection and monitoring results are greatly influenced by the testing environments. To deal with the challenges of quantitative wire rope nondestructive evaluation and SHM, a new variational mode decomposition and spectrum estimation method is proposed here. The preliminary signal analysis for typical wire broken defects under different wire rope operating speed is conducted in time, frequency and statistical domain. Then, a new quantitative wire rope evaluation method based on different defect signals features analysis, principle introduction, spectrum estimation and comparison with other mode decomposition techniques is presented, which demonstrate the feasibility and application promise for wire rope SHM and accurate evaluation.
Inspection and Maintenance Scheme Optimization Considering Atmospheric Corrosion Using Bayesian Networ
Atmospheric corrosion is a significant challenge the aviation industry faces, as it greatly affects the structural integrity of aircraft operated long after introduction. Therefore, an appropriate corrosion deterioration model is required to predict corrosion problems. However, the deterioration model is challenging to use in practice due to the limited data available for parameter estimation; thus, high uncertainty in prediction is unavoidable. To address these challenges, a method of integrating a physics-based model and a data-driven model on a Bayesian network (BN) is presented. The atmospheric corrosion environment is modeled using COMSOL, and a BN is constructed. Model calibration is performed using the collected atmospheric corrosion monitoring data at aircraft parking areas. The calibration approach improves upon existing models by incorporating actual environmental data, making it more accurate and applicable to real-world scenarios. Using the calibrated model, a method for optimizing the inspection and maintenance (I&M) scheme is described. In conclusion, our research emphasizes the importance of precise corrosion models for predicting and managing atmospheric corrosion in aircraft structures. BN that integrates physics-based and experimental monitoring data can improve the accuracy and applicability of these models, ensuring the safety and structural integrity of aircraft. And also, the results open up new avenues for future research, such as incorporating additional data sources to improve the accuracy of corrosion models further.
Performance of a Novel Inerter-Based Isolation System for Medium-Rise Buildings Under Seismic and Wind Excitations
Base isolation is a useful technique to mitigate superstructural vibrations based on the principle of shifting structural natural frequencies lower than the dominant frequency range of seismic excitations. However, excessive displacement may be concentrated at the flexible substructure, causing the loss of its building functions. Besides, the conventional isolation systems make the building structures, especially the medium- and high-rise ones, be vulnerable to wind excitations. This paper presents a novel inerter-based isolation system and investigate its seismic isolation and wind resistance performances through a 12-story RC building model. First, the isolation principle of the inerter-based isolation system is investigated based on an equivalent 2- DOF model. Also, the optimization scheme for the inerter-based isolation system is proposed. Then, the spectral representation methods (SRM) for 1d-1v and 1d-Nv stochastic processes are utilized to generate the time history samples of seismic and wind excitations, respectively. Last, the performance of the inerter-based isolation system is evaluated in time domain. The results show that the inerter-based isolation system can achieve the comparable seismic isolation effect for the superstructure without reducing the physical stiffness of the substructure so that there are not amplified displacement. Meanwhile, the inerter-based isolation system exhibits a satisfying performance under wind excitations, which means that it is a preferable isolation technique for building structures located in both seismic and typhoon fields.
Monitoring of Bolted Joints with Electro- Mechanical Impedance Spectra and Acousto-Ultrasonics
Bolted connections are one of the most widely used types of joints in various fields of applications. Their functionality depends on the conservation of the preload in the bolt’s shaft, which implies the need to monitor it. This paper addresses a monitoring procedure of the preload. Therefore, two measurement techniques for the preload are compared: On one hand, a technique, which is based on Electro-Mechanical Impedance spectra, on the other hand, a technique, which is based on Acousto-Ultrasonics. Electro-Mechanical Impedance spectra are measured with the help of the Electro-Mechanical Impedance method. This method is deployed in a relative high frequency range and the resulting spectra represent the transfer function between the local excitation of the structure and its response. In the applied Acousto-Ultrasonics procedure a burst excitation is introduced into the bolt on its head and the response is measured in pitch-catch mode at the face side of its shaft. As both techniques make use of so-called piezoelectric wafer active sensors, three different types of these transducers were used in this study and have been compared. For the experiments, three bolts having a size of M72 × 380 are equipped with the required transducers. Subsequently, they are tightened stepwise with a custombuilt testing device for bolts. The several combinations of the measurement techniques and different types of piezoelectric wafer active sensors are then compared with focus on their sensitivity and variability. The most suitable combination in terms of preload monitoring is identified.
Damage Sensitivity Study in 3D-Printed PLA of Different Infill Densities Using the Electromechanical Impedance Method
In many industries, complicated parts have been produced using additive manufacturing (AM) technology. The values of such products can significantly increase if they have fault detection and load sensing features. Modern industries, especially those with crucial applications, like aerospace and civil construction, have recently begun incorporating AM components into their designs. This necessitates the creation of precise and trustworthy methods for assessing and tracking the structural integrity of such components. Based on the local structural reaction in the high-frequency region, the Electromechanical Impedance (EMI) approach is frequently used to assess the health condition of lightweight structures with minute damage. This study investigates the sensitivity of the EMI technique to potential damage in 3D-printed polylactic acid (PLA) plates with different infill densities. Five healthy and five damaged PLA plates were printed using fused deposition modeling (FDM) with infill densities ranging from 20% to 100% at a step of 20% for the experimental study. Piezoelectric wafers (PZTs) bonded to one side of the plates were used for the measurements. Damage was mimicked by placing two strong magnets on either side of the healthy plates, and several EMI measurements were taken for the healthy states while changing the distance of the magnets away from the PZT. The conductance (G) and resistance (R) EMI measurements were fused together before the root mean square deviation (RMSD) damage index was used for the damage-sensitivity study. It was found that damage-sensitivity distance decreases with the decrease in the infill density. Further, damage-detection thresholding was investigated when using multiple sensors from identical healthy and damaged plates with printed built-in damage. The damage was detected for all the examined infill densities by the PZT positioned at a center distance of 102.5 mm from the damage.
Investigation of Impact Damage on GFRP/MWCNT for Structural Health Monitoring
The primary objective of the present study was to investigate the detection of Barely Visible Impact Damage (BVID) in composites, a crucial problem that needed to be addressed. To accomplish this, the researchers measured the resistance changes in two glass fibre layers coated with Multi-Walled Carbon Nanotubes (MWCNTs), embedded in the laminate, during the impact test. The results of the experiments performed at various impact energy levels indicated that resistance change could be effectively used to detect BVID. Furthermore, the maximum average resistance change ratio was found to be 9 % for 20J impact tests. The system's capability to detect repeated impact damage was further demonstrated through the execution of repeated impact tests. Additionally, the remaining strength values of the samples were determined by conducting compression tests after the impact tests. It was observed that the samples subjected to 20J impact damage experienced a loss of approximately 50 % in their strength. These findings emphasize the system's potential to predict the remaining strength of the material.
Influence of Uniaxial Loads on Local Resonances
Accurate and reliable nondestructive evaluation (NDE) for stress measurement is essential for assessing structural performance and preventive maintenance. Local resonances offer an efficient NDE method due to their "amplified and localized" vibration amplitude. This allows for mounting or wiring near the structural edge without affecting the vibration modes. In this paper, we investigate the influence of stress level on the local resonances in a rectangular aluminum bar structure. In the first step, we utilize the electromechanical impedance method (EMI) to extract local resonances and verified them as Zero-Group-Velocity (ZGV) mode and cutoff frequency modes by performing two-dimensional fast Fourier transform (2D-FFT) on the wavefield. Furthermore, we investigate the influence of uniaxial tensile stress on the local resonance frequencies. It is observed that both the ZGV mode and cutoff frequency mode demonstrate a measurable sensitivity to the applied axial load.
Crosstalk Effects in Matrix of PZT Sensors for SHM Applications Based on the Electromechanical Impedance Principle
Using PZT transducers leads to different structural health monitoring techniques, such as those based on Frequency Response Function and Lamb Waves. When two or more PZT transducers are closely placed, an important interference, namely crosstalk, appears. This approach verified that such an effect can be modelled and implemented as a technique to detect damage. Accordingly, this work aims to present a methodology for studying the crosstalk effect in arrangements of PZT, where the electromechanical impedance (EMI) is used as a parameter of a PZT arrangement modelled as a coupled transmission line. For that purpose, the eigenvalues of a coupled electromechanical impedance matrix are obtained. We anticipate that the structural damage will influence the EMI signatures and the modelled matrix will be distinct for the healthy and damaged conditions. The proposed methodology was tested by simulating a simple Structural Health Monitoring system based on the EMI technique. The simulated structure was considered as a homogeneous medium in which two small PZT transducers separated by 6mm were attached. Two independent damages were simulated in the structure at 10 mm and 30 mm from each PZT. The initial simulations were implemented using the software SPICE and the crosstalk was estimated in time-domain. Some metric damage indices demonstrated the effectiveness of the proposed methodology.
Electro-Mechanical Impedance Spectroscopy Leveraging Zero Group Velocity Modes for Structural Sensing
This paper presents an Electro-Mechanical Impedance Spectroscopy (EMIS) leveraging zero group velocity (ZGV) modes for active structural sensing. EMIS employs a piezoelectric wafer active sensor (PWAS) as both the actuator and the receiver to achieve structural damage evaluation via structural resonances. Distinctive from the conventional EMIS methodology, this research exploits the ZGV modes originated from localized resonance in thickness direction. The ZGV modes possess the unique property of an elapsed group velocity with a finite nonzero wavenumber and can confine the wave energy within the vicinity of the excitation source. To develop an insight into the mechanism behind the ZGV modes, semianalytical finite element (SAFE) method is implemented to extract and identify the ZGV frequencies and wave modes. Parametric case studies are performed to examine the influence of geometric and material properties change. Subsequently, fully discretized finite element (FDFE) model considering the PWAS is established to conduct coupled-filed harmonic analysis. The obtained EMI spectra verify the capability of PWAS for exciting and identifying the ZGV modes. Ultimately, the experimental validations on aluminum plates bonded with PWAS transducers are carried out to demonstrate the practical feasibility to extract the ZGV modes. The ZGV frequencies are mutually verified in comparison with the SAFE and FDFE results and are in consistency with each other. This study excavates the potential of EMIS method exploiting ZGV resonance modes for the damage characterization.
Damage Diagnosis of Complex Connection Structures of Aircraft Fuselage Stringers with Guided Wave-Based Method
For civil aircraft, structural forms of its components like fuselage and wing are very complicated. Usually, these structural components are manufactured separately and then riveted together. These connection parts, such as stringer joints and skin lap joints, are prone to be cracked and failure. In addition, most structures are inaccessible, making manual maintenance difficult. Therefore, it is of great significance to reliably monitor the fatigue crack damage of complex connection structures. Guided wave (GW) based structural health monitoring (SHM) has attracted wide attention in the aerospace field since it has the characteristics of being sensitive to small damages, and capable of monitoring large areas. However, the complex configuration, the limited space, and the interference of bolts and rivets make sensor deployment difficult. Moreover, during propagation in complex connection structures, guided waves will undergo reflection, transmission, and mode conversion, resulting in mode aliasing and wave packet deformation of response guided wave signals. The structural damage information contained in the signal will also be masked, which brings difficulties to crack monitoring in complex connection structures. Although there have been some related studies on crack monitoring of metal joint structures such as lap joints, there are still other parts in the complex joint structure of the actual fuselage, such as long stringers and belt plates. At present, especially for the multi-layer riveted butt joint structure with stringers, the research is still very limited. In this paper, the amplitude and mode of the guided wave signals of different types of channels between the components of the single strap butt joint structure with stringer are analyzed. By imposing simulated damage, the effect of the damage scattering signal on the damage identification ability after guided waves pass through multiple rivet holes or multi-layer structures of the complex structure is studied. The crack damage diagnosis method for the complex connection structure of aircraft fuselage stringer is studied and verified by the experimental results of real cracks. The diagnostic results demonstrate the effectiveness of the method.
On Measuring Material Changes at Molten Salt Reactor Temperatures in a Thermal Convection Loop with Guided Ultrasound Waves
Molten salt reactors (MSRs) for nuclear power generation contain liquid halide salts that may be highly corrosive at operating temperatures. The safety and economic viability of MSRs rely upon the continuous, in-situ characterization of the structural integrity of their conduits. Off-line nondestructive inspections of components are simply too expensive, making structural health monitoring in this harsh environment a critical technology. High-temperature capable transducers, such as fused silica optical fibers with a softening point of about 1600 °C, can be utilized to continuously monitor MSR containers. We are using a thermal convection loop (TCL) at Oak Ridge National Laboratory (ORNL) to develop structural diagnostic technology for continuous, autonomous in-situ monitoring of MSR conduits with corrosive molten salts. High temperature- and radiationresistant fused silica fiber optic sensors will measure the effects of chloride salt corrosion on alloy 600 tubing. Structural material changes in alloys containing the molten chloride salts will be measured via guided ultrasound waves in alloy tubes, detected by high-temperature capable fiber optic sensors. Distributed fiber optic sensors can localize structural changes with position-sensitive measures. The described tasks are: • launching guided wave modes at elevated temperatures in alloy 600 tubing in a thermal convection loop, and • detecting tube ultrasound responses at corrosion-prone locations in the TCL piping with fiber optic sensors. TCL studies at MSR operating temperatures could provide a basis for diagnostic systems that measure material changes in more complex structures.
Towards Passive Fiber Bragg Grating-Based Measurement of Ambient Elastic Noise for SHM Under Varying Environment
We explore here the use of fiber Bragg Gratings (FBG) transducers in optical fibers to create a minimally intrusive structural health monitoring (SHM) system. Piezoelectric transducers, commonly used for SHM, are often intrusive, so FBG transducers offer an interesting alternative thanks to their multiplexing capabilities. However, FBG can only be used as acoustic receivers and cannot emit elastic waves like piezoelectric transducers. It has ever been demonstrated in the past years that it is possible to use ambient structure-borne elastic noise, present in the structure, to retrieve the impulse response between two sensors, known as the "ambient noise crosscorrelation" method. However, passive signals are of very low amplitude, which requires a specific care in the FBG interrogation scheme, especially in realistic environment where temperature or strain variations can affect the sensing sensitivity. We present here an optimization of an optoelectronic FBG interrogation setup to measure elastic waves at ultrasonic frequencies. The system demonstrates excellent sensitivity in active signal measurements in a varying environment thanks to the realtime tracking of the setting point. Later work will present passive measurements of impulse responses under different sources of elastic noise.
Promoting Novel Strategies for the Reliability Assessment of Guided Wave Based SHM Systems
Detection of defects is of outmost importance for composite airframe due to barely visible damage which may appear at any time during lifetime. Several Structural Health Monitoring (SHM) technologies have been developed so far to continuously monitor the condition of the structure. Guided Ultrasonic Waves (GUW) are well suited for this application, but there is still a significant gap in reliability assessment and quantification of system performances. Although the theory of Probability of Detection (POD) can be used to this end, but the reliability assessment procedure needs to be properly addressed for each type of system. In this work different concepts are proposed, including two artificial intelligence approaches, tested and compared to enable a reliability assessment comparable to the conventional POD framework. On top, a novel approach on how to combine experimental data of the undamaged structure with simulations of the damaged structure is proposed. The proposed ideas are tested on a single carbon fiber composite specimen, and data is analyzed using all the different concepts. The results show that classic POD and AI-based reliability assessment can be compared as well as experimental and synthetic data can be used when the experimental variability is matched properly providing a paradigm shift in the reliability assessment field.
The Effect of Complex Corrosion Profiles on Remaining Wall Thickness Quantification Using Shear Horizontal Guided Waves
Corrosion of plate and pipe structures is a major concern to many key industries, including power, maritime, and oil and gas. When there is a need to inspect large areas quickly or when access to the structure is limited, guided wave testing is often preferred over bulk wave measurements. In this work, shear horizontal guided waves are employed for wall thickness quantification. Specifically, the interaction of modes SH1 and SH0 with complex corrosion defects is investigated. The guided wave mode are selectively generated using a phased array-based approach. A pair of identical phased array probes are positioned before and after the simulated corrosion profile, to monitor the reflected and transmitted waves. The targeted mode is excited selectively using a 32-element 3 mm pitch array and guided wave modes are decomposed after a 2DFFT is performed. The cut-off frequency technique using mode SH1 is shown to be adequate when smooth wall thinning defects are considered. When sharp pits are present, mode SH0 proved sufficient to determine the pits depth.
Virtual Frequency Data Fusion for Localization of Multiple Damages from Broadband Lamb Wave Velocity Field
In this article, a new damage indicator called reciprocal root mean square (rRMS) index is proposed for the localization of multiple damages in plate-like structures from the velocity field of guided wave propagation measured using Scanning Laser Doppler Vibrometer (SLDV). The Lamb waves are induced by applying voltage excitation to piezoelectric patch transducers pasted on the top of the plate. It is found that the velocity field measurement from one narrowband pulse excitation of a given central frequency may not be adequate in identifying the locations of all damages. To overcome this difficulty, a new methodology is proposed for a combined damage index that fuses measurement data of different excitation frequencies. However, obtaining the velocity field data at multiple narrowband frequencies from SLDV required for frequency fusion can be tedious, time consuming and expensive. To address this issue, velocity fields at various narrowband frequencies are virtually derived by utilizing the one-time physical velocity field measurement from a broadband excitation. The efficacy of the proposed method for damage localization using frequency fusion is experimentally tested. It is found that the proposed technique using rRMS index and frequency fusion is able to localize multiple damages of different types with high sensitivity.
Enhancing Guided-Wave-Based Structural Health Monitoring Using Metamaterial Devices Designed by Topology Optimization
Lamb waves inside thin-walled structures have received extensive attention due to their great promise in applications such as structural health monitoring (SHM). Applications point at the common need for effective conditioning and manipulation of the wave propagation in terms of both frequency content and mode components. In this work, the concept of metamaterials is exploited to construct functional metadevices (MDs). The MDs are designed to deliver prescribed functionalities after they are surface-mounted onto a structure conveying Lamb waves. To this end, a unified inverse-design scheme based on topology optimization is proposed and applied to achieve multifold functions such as frequency filtering and single-mode transmission. Typical scenarios with different frequencies and modes are discussed. Functional MDs with broadband working frequencies are obtained by using the established design strategy. A representative MD with a finite number of unit cells is examined through finite element simulations. Numerical simulations show that, through wave modulation of the designed MD, Lamb waves located in pass bands can transmit through the MD, while the waves within bandgaps are prohibited to propagate by the MD, which agrees well with the predicted dispersion features. An experiment is finally carried out to confirm the prescribed wave manipulation functions of the designed MD from the SHM perspective, which is finally validated experimentally using a metal specimen containing local plasticized incipient damage. This work provides a universal approach for topologically customizing MDs for the precise and tactical control of Lamb wave propagation, especially for SHM applications.
Data Acquisition and Health Analysis of In-service Railway Vehicle for Structural Health Monitoring
The enormous number of trains, increasing operation speeds, and complex line environments places greater urgency on ensuring the service safety of rail vehicle structures. This paper focuses on the application of structural health monitoring techniques to perform online monitoring of rail vehicle structural components. An onboard monitoring system was designed and installed, which uses piezoelectric sensors to perform ultrasonic guided wave acquisition, temperature compensation, and damage detection in a particular area during an online tracking test. Ultrasonic guided waves are affected by temperature and stress/loading conditions. The damage indices after temperature compensation change rapidly at different stations and different positions of the railway line, which may be affected by passenger load or line conditions, but remain at a low level after the train has entered the warehouse. The results show that the system can adapt to the railway line environment and automatically collect ultrasonic guided wave data that can be used to assess damage.
Physically Informed and Data Driven Direct Models for Lamb Waves based SHM: Advantages and Drawbacks of Existing Approaches
Validation and testing of Lamb wave based SHM algorithms requires numerous simulations that require themselves qualitatively and quantitatively consistent models representative of the physical behavior of the monitored structures and that are in agreement with experimental data. Finite elements models appear as an interesting solution to achieve this goal but are associated with large computational costs and low generalization abilities. On the other hand, data driven machine learning approaches are computationally very efficient and can predict fine details but at the cost of low physically interpretability. Original approaches trying to build physically informed models balancing the advantages and drawbacks of physics-based approaches and of machine learning approaches also exist and will be discussed in the context of Lamb waves based SHM of aeronautical structures.
Experimental and Numerical Investigations on the Acoustoelastic Effect on Lamb Waves in Aluminum
Damage detection in thin-walled components is a common application of guided ultrasonic waves, such as Lamb waves, in today’s structural health monitoring. However, the method requires that external or manufacturing influences such as residual stresses, pre-stresses or stresses due to temperature are taken into account. Therefore, this work focuses on the field of acoustoelasticity and compares the influences of pre-stress on the phase velocities of Lamb waves in numerical studies with experimentally determined results. For this purpose, previous experimental and numerical investigations of the authors are extended by expanding the frequency ranges and load steps considered. The measurements are carried out using laser vibrometry to capture the velocity field of the Lamb waves in aluminum specimens subjected to uniaxial pre-stresses covering the elastic range of the material. The experimental results are thereby characterized by the use of a 2d Fourier transform to evaluate the phase velocity over large frequency ranges. The numerical data are generated using a two-step FE model that combines a nonlinear static and a linear eigenvalue computation to determine the dispersion behavior of Lamb waves subjected to pre-stress. The subsequent comparison of numerical and experimental investigations focuses on the verification of the Neo-Hookean and Murnaghan hyper-elastic material models by comparing the changes in phase velocities induced by the pre-stress. For this purpose, the numerically obtained results for different material models are compared with experimental data. The results of these investigations provide a clear indication of the suitability of the material models used here to represent the acoustoelastic effect in Lamb waves.
Design and Application of a Statistical Learning Methodology to Remove Temperature Effect on Static Signals for Bridge Structural Health Monitoring
Infrastructures are essential for the development and flourishing of countries; in this context, bridges play an irreplaceable role as links for goods and people. Nowadays, their structural integrity is threatened by ageing and increased traffic loads: according to the American Society of Civil Engineering, 42% of US bridges are at least 50 years old. The extension of the problem requires the application of techniques for resource prioritization and effective interventions. Structural health monitoring (SHM) has emerged as a promising quantitative tool for ensuring infrastructural network resilience and optimizing maintenance. In particular, damage detection is crucial in preventing catastrophic failures and minimizing repair costs. However, the influence of exogenous factors on sensor signals often complicates the identification of damage, since they might hide the insurgence of structural anomalies. The problem is especially critical when dealing with early-stage damage, characterized by a small extension. Among such external variables, temperature, in particular, significantly disturbs SHM both on seasonal and daily levels. The latter is further exacerbated by the thermal inertia of bridges, which renders the structural behavior highly non-linear with respect to temperature and solar radiation. This study proposes a novel approach to enhance the identification capability of structural health monitoring systems by removing the temperature effect on static measures thanks to a statistical learning algorithm. In particular, signals from inclinometers and LVDTs collected for a year are considered for temperature influence removal. The method works according to a two-step process described in Figure 1. First, the seasonal dependence is removed; then, the daily influence of temperature and solar radiation is treated by exploiting a machine learning algorithm able to estimate the structural response delay caused by thermal inertia. The methodology herein presented is applied and tested on two railway bridges: a Warren truss steel bridge and a CACP bridge with simply-supported spans. In both case studies, the variability of the measured signals associated with temperature reduced substantially. Indeed, the correlation coefficients dropped close to zero, and the standard deviation of signals diminished by 80% on average. Moreover, the different structural characteristics of the two test cases proved the methodology effective and versatile, suitable for different typologies of bridges. The algorithm described in this work enhances the damage detection power of structural health monitoring systems and fits with the idea of SHM deployment on a larger scale, given its successful application to diverse structural archetypes.
An Integrated Acousto-Mechanical Energy Approach for Monitoring Flexural Bond-Slip Behaviour of GFRP Bars and Concrete
Mechanical testing data and acoustic emission (AE) data can be independently analysed to evaluate the bond deterioration process of glass fibre reinforced polymer (GFRP) rebars and concrete. However, one type of data may not be adequate for thorough and accurate damage characterization and assessment. Sentry function, a unitless quantity, which relates mechanical and AE energy, has been efficiently applied in polymer composites and multi-layered laminates to study damage progression, including delamination. The sentry function has not been used in any of the past studies to understand the bond-slip behaviour of reinforcements in concrete. The present study attempts to exploit the effectiveness of the sentry function to evaluate the flexural bondslip behaviour of GFRP rebar and concrete. A thorough AE monitoring experimental programme has been conducted to evaluate and understand the interfacial bond deterioration process between GFRP reinforcement and concrete under flexural loading. The experimental test variables include confinement conditions and embedment length of the GFRP bar. Findings from the study suggest that the sentry function proves to be a successful and effective tool for evaluating the GFRP-concrete flexural bond deterioration. The sentry function captures and delineates the development and utilization of various bond stress transfer mechanisms (chemical adhesion, mechanical interlocking, and frictional resistance) involved in bond-slip behaviour.
Damage Identification for Guided Wave Testing of Composite Structures Using Statistical Features
Guided wave structural health monitoring (GW-SHM) is essential for detecting damage in composite materials. Conventional damage identification approaches require knowledge of material properties to calculated deviation of monitoring signal from baseline and are limited to specific damage types or materials. Deep learning has emerged as a more automated method, but it requires high computational power. To address this, we propose using two features: correlation coefficient deviation (CCD) and root mean squared deviation (RMSD). CCD captures the changes in phase of monitoring signal due to presence of the damage. RMSD on the other hand is sensitive to changes in the amplitude. When combined with a binary random forest classifier, these features achieve performance comparable to deep learning. We tested our algorithm on two datasets with different damage types, recording accuracy of 94.4% for Open Guided Waves (OGW) dataset and 99.2% for NASA Prognostic Center of Excellence-Guided Waves (PCoE) dataset. These lightweight models are suitable for in-situ monitoring, offering practical application for damage identification.
A Hybrid Surrogate Modeling Method for Corrosion Morphology Prediction Under Non-Stationary Dynamic Loading
The loads on the structures or components are more often fluctuating stochastically over time and exhibit non-stationary statistical behaviors. The goal of this research is to predict the corrosion growth of large civil infrastructure using multi-scale simulations with the consideration of time-dependent stochastic loads along with other sources of uncertainty. A stochastic process modeling method is first employed to model the stochasticity of the load conditions based on real-world data. A multi-scale simulation model is then constructed to predict the corrosion morphology evoluation over time subject to dynamic loads. The model consists of a mesoscale phase-field simulation and a macroscale structural analysis model. The required high computational effort of the multi-scale simulation model makes it unsuitable for probabilistic analysis purposes, which requires the execution of the model thousands of times. This research proposes a hybrid surrogate modeling to substitute the original multi-scale simulation model. The hybrid surrogate model consists of a deep neural network-based autoencoder for dimension reduction and a Gaussian process regression-based model for forecasting and uncertainty quantification. A navigational lock miter gate case study is employed to demonstrate the efficacy of the proposed method.
A Reduced-Order Digital Twin for Structural Health Monitoring of Steel Bridges
Conventional simulation methods, such as the Finite Element method, are less suitable for efficient and adaptive digital twins of large-scale structures needed for structural health monitoring (SHM). The complexity and scale of such structures require new simulation techniques that can handle the increasing computational demands and provide accurate results in real-time. This paper presents a novel approach for the implementation of a digital twin for SHM of steel bridges using reduced order modelling. For this purpose, the commercial software Akselos originally developed for digital twining in the aerospace industry is herein adapted to a structural engineering context. The parametric and component-based approach allows a modular structure of the digital twin with the possibility for quick adaptions according to the SHM data. The method is applied to a steel arch bridge as a case study. The example bridge was monitored over the period of a month using strain gauges at five critical locations together with temperature sensors. In addition, a targeted loading test with a truck was performed. The collected monitoring sensor data is processed and merged into the digital twin. This integration enables precise predictions about the bridge’s structural integrity, maintenance and repair planning as well as possible future damage locations. The proposed approach demonstrates the potential of the digital twin for real-time monitoring and prediction of changes in the structural integrity of large-scale structures, providing a promising solution for efficient and effective SHM of steel bridges.
Vision-Based Displacement Estimation of Large-Scale Infrastructure—A Case Study
Vision-based displacement measurement is promising for infrastructure applications because of its ability to cover traditionally hard-to-access regions, as well as its ability to enable simultaneous dense displacement measurements over large areas within a short time period. This paper proposes, demonstrates, and evaluates a visionbased strategy to estimate the displacement of large civil infrastructure under in-service loading. The first step of the strategy consists of conducting a photographic survey of the structure during a typical loading event. Next, a Kanade-Lucas-Tomasi (KLT) feature tracker is employed on photos to track displacements at various locations on the structure. Finally, computer vision-based techniques including camera motion compensation with 2D geometric transformations, correction for lens distortion, and localized histogram equalization of image intensity are implemented to tackle the inherent challenges with field-collected image data. This paper demonstrates the proposed vision-based strategy on a large, steel miter gate at the lock and dam on the Columbia River at The Dalles, OR. To evaluate the accuracy of the displacement estimation strategy, virtual images of a 3D photorealistic model of the gate with known displacements from a finite element analysis (FEA) model were considered. These displacements were then compared to the FEA displacements projected from the 3D model onto the image plane. The differences between these displacements, which ideally should be zero, directly indicate the error of the proposed strategy. Subsequently, displacements estimated from the field images are compared to those predicted by the FEA. Future work will investigate using these differences to generate high-density data for use in Bayesian model updating. The proposed approach is readily adapted for understanding the deformation of other large-scale civil infrastructure systems under inservice loading and preparing for a FEA model-updating schema.
Review of Various Curing Processes and Techniques of Printed Circuit Board (PCB) and Introduction of New Innovative Thermal Curing Technique
This paper examines the history of curing methods of Printed Circuit Boards (PCBs), especially, the thermal-based curing method with its different curing process techniques. In addition, highlighting its importance, hence, an incomplete curing process can lead to delamination, cracking, or other failures, which render the PCB useless. Moreover, various techniques used in the thermal-based curing method with their advantages will be explained. Also, the limitations of each technique will be demonstrated, such as, long curing times, uneven temperature distribution, high energy consumption, and absence of the knowledge-base of different PCBs, like board volume, heating capacity, and electronic elements. Furthermore, factors that affect the thermal-based curing method like type of material used, humidity, temperature, and time duration will be presented. So, each factor must be carefully controlled to ensure that the curing process is effective and consistent. In addition, a new innovative thermal-based curing approach will be introduced to overcome these limitations by using Artificial Intelligence (AI) techniques for the indirect prediction of temperature on PCBs. The proposed approach involves the use of new proper control based on AI technique to obtain an advanced control technique to control the heating process of PCBs during the curing process. This new approach will be developed during an ongoing research and development project.
Efficient Lifecycle Reliability Assessment of Offshore Wind Turbines using Digital Twin
The objective of this study is to develop a reliability assessment framework for the maintenance optimization of offshore wind turbines (OWTs) using an uncertainty-aware digital twin framework. OWTs are typically located far from the coastline to optimize wind utilization efficiency and minimize disruption of human activities. However, the greater distance between OWTs and the coast can increase maintenance costs due to accessibility and exposure to harsh weather conditions. Effective planning is crucial in managing these costs. Therefore, employing digital twin models for OWTs can provide potential benefits. A digital twin framework creates a virtual replica of the turbine and leverages multi-source data for real-time simulations, enabling assessment of the turbine’s performance under various loading conditions, which can substantially enhance the maintenance decision-making process. The proposed framework has two main contributions: (1) uncertainty quantification in the long-term performance of OWTs at both the component and system levels, and (2) digital twin decision support leveraging OWT failure probabilities under various scenarios, which is used to provide maintenance recommendations aimed at optimizing system profitability and structural integrity. Additionally, the digital twin model provides clear and concise warnings regarding potential OWT failures.
Towards Dynamic Digital Twin for Monopile-Supported Offshore Wind Turbines
Offshore wind turbines (OWTs) are critical in achieving the global goal of net zero greenhouse gas emissions. However, they face significant safety and reliability challenges due to harsh environmental conditions and complex evolution mechanisms of structural properties. To address these challenges, dynamic digital twin technology is proposed as a potential solution to accurately monitor structural performance and condition variations. It can contribute to real-time tracking of OWTs' performance, early detection of potential structural damages, and accurate estimation of remaining useful life.
Evolutionary Sensor Network Design for Structural Health Monitoring of Structures with Time-Evolving Damage
Structural health monitoring (SHM) aims to assess damage intensity and provide engineers with data to make informed maintenance and repair decisions. SHM systems collect crucial information for evaluating a structure's current state, enabling appropriate maintenance decisions and loss mitigation. Therefore, it is crucial to acquire damage-sensitive data by using a well-designed SHM system that is optimal in terms of expenses as well as functionality. In this research, we present an optimal sensor placement framework that considers two stages of the structure’s lifespan: (1) an early-stage pre-posterior design, and (2) a periodically updated sensor design in the operational stage. When the sensors are designed initially in the pre-posterior stage, there is no data available to make informed design assumptions for designing the SHM system. As a result, all the uncertainties and damage evolution models for the structure need to be modeled probabilistically based on reasonable assumptions derived from historical perspective and engineering judgment. The early-stage design of an SHM system initiates the data acquisition and serves two primary purposes: (1) helps update the current state of the structure, and (2) supports data-informed maintenance decisions. As the structure degrades over time, despite periodic maintenance, it is bound to approach the limiting or critical damage state. This warrants an even better inference of damage state with the goal of avoiding the worst scenario of failure. In addition, another reason to update the sensor design while in the operational stage is to optimize the SHM system by making it more suitable to the current structural state and benefit from the data acquired through the pre-posterior design. Periodically updating the design yields the best risk to reward for an SHM system in terms of its expenses and functionality. We demonstrate the application of the proposed framework on a realworld complex miter gate structure.
Degradation Model Updating for Failure Prognostics Using a Sequential Likelihood- Free Bayesian Inference Method and Video Monitoring Data
Structural systems are inevitably subject to degradation that evolves progressively over time. Developing a degradation model to capture the physics of damage evolution is essential for failure prognostics, i.e., remaining useful life (RUL) prediction, to enable individualized predictive maintenance. Due to the lack of runto- failure data for large structural systems and natural variability across physical systems, uncertainty is inherent in the degradation model even if a degradation model can be constructed based on the physics of a certain damage mechanism. It is therefore necessary to update the degradation model over time based on measurements of quantities that are directly measurable. With the development of sensing and image processing techniques, it is possible to derive structural strain response from videos, which overcomes the limitations of the cumbersome and costly deployment of conventional contact sensors. While the strain video monitoring data provide rich information for structural health monitoring, the usage of this information for degradation model updating is challenging due to the implicit connection between the degradation model parameters and strain video monitoring data and the highly complicated model architectures. This research proposes a novel sequential Bayesian model updating framework for a degradation model using a likelihood-free Bayesian inference method and strain video monitoring data. In the proposed framework, strain video monitoring data are first compressed into lowdimensional latent time-series features using a convolutional autoencoder. Subsequently, a likelihood-free Bayesian inference method is employed to update the degradation model using a given time duration of the monitoring data. To enable continuous monitoring and model updating over a long time period, a sequential Bayesian model updating scheme is developed. Based on the updated degradation model, failure prognostics are performed sequentially and the associated uncertainty on RUL estimation is also quantified. The application of the developed framework to a miter gate structure demonstrates the efficacy of the proposed framework. Keywords: Remaining useful life; Degradation model; Likelihood-free Bayesian inference; Conditional invertible neural network
Automated Crack Detection for Underwater Inspection of Miter Gates with Unmanned Underwater Vehicle
The emergence of Unmanned Underwater Vehicles (UUVs) as tools for underwater inspection tasks presents promising potential. This study presents a novel automatic damage detection framework in large-scale underwater structures using a physics-based graphics model (PBGM). A high-fidelity finite element model of the Greenup Miter gate on the Ohio River is utilized to provide the fundamental information for the graphical model development. An open-source software is used as a graphic-based observational system to render images given different inspection distances and environmental conditions, such as light conditions and water quality. A deep neural network for crack detection with segmentation from the existing literature is adopted and then trained using transfer learning, adapting it to the unique conditions of underwater circumstances. Results indicate that the proposed method provides highaccuracy damage detection amidst the unique background noise and uncertainties presented in the underwater environment, contributing significantly to the field of UUV inspection of large-scale structures.
Detection of Buckling in Columnar Structures by Shape Sensing with Multi-Core Optical Fibers
This paper presents the results of a demonstration experiment in which helical multi-core optical fiber shape sensing was applied to detect the buckling deformation of a columnar structure, one of the most basic structures, while preventing fiber breakage. The experimental results show that the buckling deformation of a rod can be detected even when only a part of the fiber is fixed to the measurement target, suggesting its applicability to structural health monitoring of columnar structures.
Developing an Experimentable Digital Twin of a Novel Forestry Machine: Application, Experiences, and Benefits
The forestry sector in central Europe aims for a fully mechanized wood harvest. This goal is currently restricted by the limited reach of forest harvesters on the market and legislative specifications on the distance between skid trails. As a consortium of industry and academia, we are developing a novel forestry machine that is designed to fell and skid trees in the inter-field between skid trails and which is equipped with a lightweight boom for an extended reach; to date, felling in the inter-field is not reachable for harvesters and is done in manual labor. As a key risk, overturning the whole forestry vehicle prevents a transfer of current harvester designs to an extended reach. To avoid overturning, we apply a consequent lightweight design for the boom of the new forestry machine. The development of the forestry vehicle will be supported by a versatile use of Experimentable Digital Twins (EDT), which are one promising concept to push the idea of Digital Twins into practice. The EDT allows for interaction in a pure virtual or in a hybrid environment. The tasks of EDT in the context of the new forestry machine will include a virtual prototype in the development phase, a driver assistance system in the operational phase, and a central hub for a strain-based monitoring system in the operational phase. This contribution gives an insight into our present work on the EDT of the novel forestry machine, emphasizing the load monitoring system of the lightweight boom. On a conceptual level, we present the load monitoring system, addressing the challenges specific to this application. We outline the proposed strain sensor distribution and describe the interaction with the EDT.
Finite Element Model Updating Using Primal-Relaxed Dual Global Optimization Algorithm
Finite element (FE) modeling has become a powerful tool in predicting the response of various engineering structures. However, predictions from the numerical model often differ from in-situ experimental measurements due to numerous approximations and inaccuracies in the model. The in-situ experimental data obtained from the as-built structure can be used to update selected model parameters to obtain a more accurate FE model that truly reflects the behavior of the as-built structure. This research investigates FE model updating by the modal property difference approach using eigenvalues and eigenvectors. The modal property difference approach is a nonconvex optimization problem, for which generic solvers cannot guarantee global optimality. However, the problem can be reformulated into a biconvex problem so that the global optimum can be found using a primal-relaxed dual (P-RD) decomposition approach. The formulation of the model updating algorithm and examples that demonstrate its functionality are presented in this paper.
Operational Health Monitoring of Bridges Using Bayesian Model Updating and Computer Vision Techniques
This study presents a new framework for operational health monitoring of bridges using computer vision and finite element (FE) model updating techniques. In this framework, the bridge acceleration responses and vehicle tracking data are obtained under operational conditions using accelerometers, regular traffic cameras and computer vision techniques. These data are synchronized and integrated with the initial FE model of the bridge through a FE model updating process to jointly estimate the dynamic load of tracked vehicles on the bridge and the unknown FE model parameters. The final estimates of FE model parameters reveal information regarding the location and extent of potential damage in the monitored bridge. This framework is first successfully verified in a numerically simulated environment and then validated using a real-world bridge subjected to traffic excitation due to passage of tri-axle dump trucks.
Physics-Informed Neural Network for Analyzing Elastic Beam Behavior
This paper introduces a methodology that combines a physics-based model with observed data for accurately modeling the deflection of an elastic beam in the context of structural health monitoring. The challenges associated with physics-based and databased methods such as computational time, simplifying assumptions, and seamless integration of sensor data with physics-based models are addressed. The presented method offers a promising approach by effectively fusing data with prior physical knowledge in a cost-effective manner. The proposed methodology is validated through comparisons with analytical and finite element analysis methods for beams with various irregularities such as point loads and supports. The results demonstrate the advantages of integrating sensor data into the model for faster convergence and improved accuracy.
Vibration Based Damage Identification in Welded Asymmetrical Steel Frames Using Regularization Techniques
The damage identification problem for a frame structure can be formulated in a linear equation in the form Aθ = b. In this formulation, matrix A represents the FE model parameters, vector θ represents the change in state variables or the mathematical parameters which are to be determined, and vector b represents the response to an external excitation. In the real test case, the matrix A can be rectangular, and the linear relation is an over-determined and ill-posed problem. The results of these ill-posed problems are non-continuous and thus require special solution techniques like regularization techniques for getting a solution. This study is aimed at damage identification based on the vibration analysis of asymmetrical multistory plane frame structures with welded joints. Time-domain response analysis assisted by various regularization techniques is used to identify the damage in the portal frame structures, which is caused due to loss of stiffness. Initially, the effectiveness of the regularization method is examined by considering a one-story steel frame with welded connection, which is then analyzed analytically with FE formulation by simulating single damage cases. The analysis has been extended for an instrumented multistory asymmetrical plane frame structures for single damage cases with static analysis. Various regularization techniques like Lð regularization or Least absolute shrinkage and selection operator (LASSO), Lï regularization, elastic net regularization, and linear regression are used to study their effectiveness in the damage identification without doing model updating of the numerical model. In the study, the Lð regularization gives better results than other methods for a single damage case. The damage identification in asymmetrical steel frames needs to be better studied, and this study can be replicated for more complex steel structures for real-life damage scenarios. This can be developed as a real-time standalone early warning system for important steel frame structures, thus ensuring their timely maintenance and continuous sustainable use.
Structural Damage Detection Through Finite Element Model Updating Using Evolutionary Algorithm
Structural damage can be caused by a variety of factors, which include internal factors such as design flaws, construction errors, material deficiencies, and external factors such as earthquakes, overloading, environmental factors, and terms of service violations. As such damage can compromise the safety, functionality, and financial viability of a structure, it is essential to identify and address any issues quickly. The engineering community, including civil, mechanical, and aerospace engineers, is greatly interested in structural damage identification. Numerous methods, such as experiential and simulation-based techniques, have been developed to achieve this goal. However, accurately modeling a structure and developing a robust inverse algorithm for damage detection is a significant challenge. Finite element modeling is one of the widely used methods for detecting structural damage. Here, Model updating is a crucial process that involves adjusting a structural model to improve its accuracy and reliability. Various methods for updating Finite Element Models have been proposed, including sensitivity-based, direct, statistical, probabilistic, and iterative methods. However, traditional methods can be complex and time-consuming, leading researchers to explore the use of evolutionary algorithms to streamline the process. Evolutionary algorithms are a type of computational optimization technique that mimics the process of natural selection to find the best solution to a given problem. In the context of model updating, evolutionary algorithms can identify the optimal combination of model parameters that best matches measured data. There has been a growing interest in using evolutionary algorithms to update Finite Element Models for structural damage identification in recent years. This paper presents a case study on damage identification, specifically sectional loss and boundary condition rigidity, as an optimization problem. It demonstrates the use of an evolutionary algorithm to update a Finite Element model for structural damage detection and discusses the advantages of using this algorithm over traditional methods. The abstract also highlights some of the key challenges associated with using evolutionary algorithms for model updating, including the need for accurate and reliable data and the need to carefully tune algorithm parameters.
Development of Deep Neural Network-Based Strain Values Prediction Models for Full-Scale Reinforced Concrete Frames Using Highly Flexible Sensing Sheets
Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. These include abnormal changes due to strain fields and abnormal symptoms of the structure, such as damage and deterioration. At present, large-scale deployment of sensors in existing structures to cover large areas is still difficult to overcome, while increasing maintenance costs. In this study, a strain sensing sheet with high tensile strength is used to collect the strain data set generated on the concrete surface of the full-scale reinforced concrete (RC) frame structure when the cyclic load is applied to its limit. On this basis, two prediction models of deep neural network for frame beam and frame column are established. The training results show that they can predict the strain value accurately and have good generalization ability. These two deep neural network prediction models will also be deployed in SHM systems in the future as part of the intelligent strain sensor system.
Design of Multifunctional Structural Battery Composites for the Next Generation of Electric Vehicles
A multifunctional energy storage composite (MESC) combines the high energy density of lithium-ion batteries with the structural benefits of carbon fiber composites, resulting in a lightweight structural battery with excellent mechanical strength and enhanced safety for electric vehicles (EVs). This paper presents the design-tofabrication development of new MESC, with a specific focus on the design, fabrication, and testing of an electronic skateboard (e-skateboard) as a representative system-level study for the EVs. A finite element analysis (FEA) is employed to design the MESC eskateboard. The FEA of the MESC e-skateboard is validated through mechanical testing, demonstrating a significant 24% reduction in weight, leading to an impressive 31.6% improvement in energy density compared to a conventional e-skateboard. As a part of the MESC technology, an embedded smart sensor network is also developed for the e-skateboard, showcasing the capability for real-time battery monitoring, and estimating its state of charge during in-service operation. The promising outcomes of the new MESC technology position it as a novel solution for enabling multifunctional structural battery composites in future electronic vehicles and aircraft applications.
High-Dimensional Data Analytics for Sparse Recovery of Guided-Waves Dispersion Curves Using B-Splines
This research presents a technique to recover the dispersion curves of guidedwaves by utilizing the inherent sparsity of these signals in the frequency-wavenumber domain. The proposed methodology is a data-driven approach that combines physicsbased knowledge with high-dimensional analysis to obtain the dispersion curves of the medium from experimental signals. Initially, a sparse two-dimensional dispersion matrix is constructed using sparse wavenumber analysis. Then, B-splines are fitted to non-zero elements of this matrix to establish an initial estimate of the dispersion curve parameters. These parameters are further optimized using the quasi-Newton algorithm to improve the accuracy of signal prediction. The results demonstrate that this method can significantly reduce the dimensions of Lamb waves signals to approximately 0.05%, which avoids overfitting. The retrieved signals in the frequency-distance domain exhibit a correlation of approximately 50% with the original signals. In comparison with sparse wavenumber analysis, this technique requires two orders of magnitude fewer parameters to represent the medium's dispersion curves.
Intelligent Manufacturing Principles for Structural Health Monitoring in Advance Composite Structures: A Machine Learning- Based Driven Placement Approach
This paper presents a fundamental investigation into the composition and placement of mechanoluminescence smart material ink to develop a predictive model framework for machine learning-based lead placement in structural health monitoring of nextgeneration fabrication methods. To address the need for multifunctionality and structural health monitoring capabilities in next-generation composite structures, this study proposes a closed-loop integration of material-driven design, multi-modal additive processing, and intelligent manufacturing principles. The proposed method utilizes mechanical simulations and in-situ sensing to establish a priori knowledge to optimize the placement, orientation, and manufacturing conditions for smart sensor embedment to maximize sensing efficiency and part performance. Experimental validation involved the hybrid machining of compact tension samples to produce a crack front in additively manufactured material. Microfluidic deposition facilitated mechanoluminescent sensor embodiment near the crack front, and the sample was then subjected to a fracture mechanic analysis, whereby the crack growth rate and direction was monitored. The resulting light emission from the sensor was captured using a mixture of high-speed video and optical sensors to determine the mechanical energy transferred during crack propagation. The resulting dataset informed an algorithmic approach to govern the placement and process parameters of the embedded mechanoluminescent sensors throughout the polymeric structure. This approach enriches the knowledge pool of digital design discovery, showcasing the capabilities of material-driven learning schema for multifunctional composite fabrication. Our investigation provides important insights into the design and development of next-generation materials with enhanced structural health monitoring capabilities. This approach provides a valuable tool for the real-time monitoring of crack propagation and material failure, which has important implications for the development of safe and reliable structures in various industrial applications.
Integrated Sensing System for Automotive Applications
The Department of Energy (DOE) Vehicles Group has identified a need for novel multifunctional composite materials and structures for the automotive industry that have the capability to reduce weight and volume as well as costs of “conventional” structural components by performing engineering functions beyond load carrying. As part of this project, Acellent in collaboration with Ford Motor Company is developing a sensing system for use in automotive applications. An advanced sensing system is needed to effectively distinguish impacts between different objects. Information received from the first contact between object and automobile bumper can be used to identify the object for vehicle applications. This paper will discuss the need and requirements for such a system and provide an overview of the development of a piezoelectric sensor system including the design, development and initial testing of a complete prototype of the system through various tests on bumpers. Acellent’s SMART layer sensor design for the bumpers will be discussed along with their manufacturing and installation on the car bumper. The paper will also discuss potential optimization of the sensor locations. Detection algorithms were developed to distinguish between various objects that were used in the impact on the bumper. Additionally key parameters were considered when building the algorithms: • impact speed, • bumper stiffness and dimensions, • object stiffness, mass and size Testing was conducted using, for example, simulated small animal, stone, wooden board etc. Results from the testing of the complete prototype system for the impact detection for automobile applications will be presented.
Covariance of Limit Defining Pairs (CLDP): A Novel Approach to Establishing Detection Sensitivity for Structural Health Monitoring Data
This paper explores a novel approach to establishing detection sensitivity for SHM. The proposed approach uses data associated with the largest flaw such that all smaller flaws were not detected and the smallest flaw such that all larger flaws were detected for that specimen. These points are termed “limit defining” because they directly contribute to defining the most valuable portion of the probability of detection (POD) curve. A POD curve can be efficiently built using a standard hit/miss analysis, and an upper confidence bound can be generated by calculating the Covariance of the Limit Defining Pair (CLDP) to take the dependence into account. A fatigue experiment was conducted on 60 aluminum bars using a carbon nanotube (CNT) based fatigue crack gauge. 100 data points were collected for each specimen as the crack grew to ~2mm. 1000 classic POD curves were generated by randomly selecting 1 point from each specimen to observe variability, and subsequently the same data was analyzed using the CLDP. Finally, random subsets of specimens were selected to simulate POD statistics associated with testing fewer specimens. Overall, CPLD has proven to be a robust and reliable alternative to traditional detection sensitivity evaluation for SHM sensors.
Assessing the Performance CVM Sensors for Monitoring the 737 Aft Pressure Bulkhead
Current maintenance operations and integrity checks on aircraft require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be removed, sealant must be removed and disassembly processes must be completed. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Reliable Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition, and signal the need for specific maintenance actions. This paper presents an OEM-airline-SHM vendor-regulator effort to realize these benefits by moving Comparative Vacuum Monitoring (CVM) technology into routine use in airline maintenance programs. A certification program has been completed to validate CVM sensors for surface crack detection on the 737 Aft Pressure Bulkhead (APB). Formal and comprehensive CVM technology validation and certification was guided by a recently-released FAA Issue Paper which addresses the full spectrum of issues including design, deployment, durability and performance. For accurate SHM validation to occur, all relevant environments - which may include separate fatigue and environmental response components - were properly simulated in the tests. Flight tests also played an important role in assessing overall CVM system performance under normal aircraft operations. Validation tests were designed to address the CVM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, and the potential inspector population. The test results will be presented in light of the overall CVM certification plan. Such SHM deployment programs are allowing the aviation industry to confidently make informed decisions about the proper utilization of SHM. These programs also streamline the regulatory actions and certification measures needed to ensure the safe application of SHM solutions.
Robotic End-of-Line Testing for Hydrogen Pressure Vessels Using a Strain-Based SHM System
High safety factors lead to increased material input and costs for Composite Overwrapped Pressure Vessels (COPV) in hydrogen mobility. Reducing safety factors in the form of material can improve profitability, but reliability must be maintained. Currently, there is no automated end-of-line testing that can help quickly and reliably check the structural integrity and enhance testing scalability for COPVs. Our novel approach to COPV quality assurance uses robotically applied test loads to inspect the structural condition of each COPV. It captures the structural response using a strain-based SHM system. The design of the sensor layout for this application faces two central challenges: selecting an efficient combination of measuring points, damage indicators, and appropriate test loads and ensuring the system is functional as an onboard SHM system. In the context of this conference contribution, we explain the mentioned quality assurance approach in more detail. Additionally, the first results in the design of the sensor layout, which measures the structural state of COPV in robotic testing and onboard operation, are presented.
An Investigation of the Effect of Measurement Interval on the Autoencoder Based Damage Detection in Uncontrolled Structural Health Monitoring
Unsupervised damage detection in uncontrolled, outdoor environmental and operational conditions (EOCs) is crucial for practical structural health monitoring. While previous research has explored autoencoder-based unsupervised damage detection methods, they require training data only from pristine conditions. In long-term monitoring, irregular environmental and operational conditions, as well as variations in damage, may make it difficult to satisfy this requirement. In this paper, we propose a novel autoencoderbased approach that uses training data containing regular and irregular environmental and operational conditions, as well as damage variations. We also investigate the impact of various factors, such as training epoch, damage duration, and measurement interval on the accuracy of damage detection. Our results indicate that our proposed framework achieves an AUC score of over 0.95 when the measurement interval is around 860 seconds per measurement. Interestingly, this score decreases both when we sampling faster and slower.
Optimization of a Vision-Based SHM System for Imaging Subsurface Impact Damage in Composite Structures
This paper describes the optimization of a computer vision-based technique for visualizing subsurface barely visible impact damage (BVID) in composite structures. The system uses a piezoshaker for exciting a guided wavefield with a sweeping frequency in the near-ultrasonic frequency range, a digital camera or stereo-camera for recording surface dynamics, and an energy-based damage imaging condition for identifying local resonance from standing waves within damage regions. The method for extracting surface dynamics from the videos was optimized, evolving through three techniques – 3D digital image correlation (DIC), off-axis 2D DIC, and image decorrelation – with the ultimate goal of creating a practical and efficient visionbased method for BVID inspection. Each proposed technique was applied to inspect two CFRP composite-honeycomb panels that had been subjected to low-velocity impacts. Damage images produced with all techniques for a 100-mm  100-mm FOV using a three-second video show accurate damage imaging ability. The processing time required by the optimized decorrelation technique for extracting surface dynamics is approximately 15 times less than 3D DIC and 5 times less than off-axis 2D DIC. The increased efficiency, reduced complexity, and demonstrated accuracy of the system suggests a high potential for practical baseline-free and in-time computer-vision-based structural health monitoring (SHM) and baseline-free subsurface damage imaging for critical composite structures.
Analysis of Reliability and Effectiveness of Repeated Inspections Based on Probability of Detection Method
Structural health monitoring (SHM) performs the function of evaluating performance and durability for structural life management by monitoring changes in engineering structures such as buildings and bridges. In order to obtain information about a structure's ability to perform its intended function, data collection activities are required through various inspections aimed at detecting the presence of structural damage. Repeated inspections have been proposed to increase the reliability of SHM. Many people considered repeated inspection as a way to increase the chance of detecting damage. If more than one of the individual inspections finds damage, collectively evaluates the damage as detected and produces the results of repeated inspections. Probability of detection (POD) was used as a measure of the sensitivity and reliability of the inspection process. To evaluate structural condition and predict remaining service life, POD is measured and structural life is calculated based on initial defect sizes that are just below the inspection limits of non-destructive testing techniques. Repeated inspections can be considered by multiplying the likelihood function, but if a single inspector performs repeated inspections, they may not be independent because they may be biased by previous inspection results. The repeated inspections is independent if performed by an automated system or another inspector unaware of previous inspection results. It can be assumed that each inspection is independent in that the SHM system can automatically collect data even in areas where general non-destructive testing is impractical due to complex geometries and accessibility limitations, but conversely, due to the dependencies of the data, there is no statistical difference between subsequent measurements. It is also considered to be less independent. In this paper, the effect of repeated inspection on POD improvement was confirmed using eddy current inspection data, and the benefits of repeated inspection differed from those predicted by assuming complete independence. Furthermore, the effectiveness of repeated inspection was discussed.
Least-Square Support Vector Regression for the Prognosis of the Deteriorating Structure Under the Seismic Excitations Using Autoregressive Model
In the current work, the prognostic behavior of the degradation in the space frame is carried out through the proposed data-driven framework based on autoregressive modelling and least square support vector regression. The acceleration responses were obtained at certain intervals after introducing the time dependent damage in the building. These responses are used to develop the damage index through filtering and statistical measures. The series of damage index is nonstationary in nature and therefore, the time varying autoregression (TVAR) modelling is carried out to obtain the change point. Next, for the prognosis, the algorithm performs surrogate modeling of observed degradation through least square support vector regression (LS-SVR) and the same is used to predict the trend of degradation. To increase accuracy and obtain the confidence bounds, a few mini datasets are created through omitting and shuffling the observation in the training dataset. The LSSVR predicts the degradation trend through each mini dataset. The mean and covariance of the prediction provides the best fit value along with upper and lower bounds. The advantage of SVR is that it can provide high-order approximations with sparse availability of samples. However, SVR optimized through sequential minimal optimization is time consuming and iterative in nature. Therefore, in the present study least square SVR is used for the model fitting and regression. Overall, the results highlight the potential advantage of creating mini datasets for taking advantages of LSSVR for the prognosis and obtaining the confidence bounds.
Autonomous Monitoring of Breathing Debonds in Bonded Composite Structures Using Nonlinear Ultrasonic Signals
This study aims to develop a structural health monitoring model that autonomously assesses breathing-type debonds between the base plate and stiffener in lightweight composite structures. The approach utilizes a specifically designed deep learning architecture that employs nonlinear ultrasonic signals for automatic debond assessment. To achieve this, a series of laboratory experiments were conducted on multiple composite panels with and without base plate-stiffener debonds. A network of piezoelectric transducers (actuators/sensors) was used to collect time-domain guided wave signals from the composite structures. These signals, representing nonlinear signatures such as higher harmonics, were separated from the raw signals and transformed into time-frequency scalograms using continuous wavelet transforms. A convolutional neural network-based deep learning architecture was designed to extract discrete image features automatically, enabling the characterization of composite structures under healthy and variable breathing-debond conditions. The proposed deep learning-assisted health monitoring model exhibits promising potential for autonomous inspection with high accuracy in complex structures that experience breathing-debonds.
Structural Performance Monitoring Employing Linear Observer
Structural performances are heavily relying on the overall health conditions (damaged or healthy) of any dynamical system. Hence, it is essential to keep them monitored to avoid any partial/fully damaged situation. Due to the availability of the modern technologies, the monitoring tasks are done by employing sensors to reduce manual effort. However, the structural health monitoring via sensors deployment comes with a cost even considering all the merits of modern monitoring approach. Therefore, it is realistic to have a reasonable number of sensors on the structures in order to avoid financial hurdle or to make things more feasible. In order to minimize the sensors number, in this study, the investigations have been done via employing finite number of sensors. As a result, it might be tricky to obtain the missing states of the degree-of-freedom where sensor was not placed. Herein, the missing states are estimated by adopting a linear type observer e.g. Kalman filter as all of the states have not been observed. The numerical simulations have been performed by considering a 7-storey structure in a nearly real-time scenario via the use of MATLAB and SIMULINK. To achieve the optimal performance, the Kalman Gain was also estimated real-time by solving the Riccati equation of the investigated system. The performance of the investigated problem has been evaluated under healthy and different equivalent damaged conditions by adding external noise quantities to the healthy signals. In a nutshell, it is observed that the observer is capable of rendering the original behavior of the structure quite accurately under both healthy and damaged conditions. However, it has been observed that with the significant level of noise the observer struggles a lot attain optimal performances.
Generation of Mode-Selective Frequency Response Functions from Temporally Sampled, Broadband and Multimodal Guided Ultrasonic Wave Signals
Structural health monitoring (SHM) systems for thin-walled, large-scale structures using guided ultrasonic waves (GUW) have recently been complemented by methods based on frequency response functions (FRF). These approaches enable the calculation of the structure’s response to any artificially generated excitation signal within the frequency range under investigation. This study presents a novel concept for extracting separate FRFs for the two fundamental modes from temporally sampled, broadband and multimodal GUW signals. In this work, quasi-ideal impulse response functions (IRF) are generated by retransforming multimodal broadband FRFs into the time domain. These FRFs are recorded energy-efficiently using sweep excitation and contain all occurring GUW components at the sensing position. The extended dispersion based frequency-domain intrinsic component decomposition (DBFICD) algorithm and the iterative frequency-domain envelopetracking filter (IFETF) algorithm are used to extract the corresponding signal components from the IRFs. Based on their different dispersive behaviour they can be assigned to the fundamental modes and combined to form the two respective IRFs. Transforming the latter back into the frequency domain results in two separate FRFs. This allows damage detection algorithms to estimate the structure’s mode-selective response to any selected signal within the frequency range under investigation. The method is validated experimentally by recording multimodal and single-mode FRFs in an aluminium plate with a mode-selective, two-sided piezoelectric actuator setup and a laser Doppler vibrometer. The estimated single-mode structural responses based on the FRFs extracted by the presented approach agree well with the actual responses to mode-selective excitation. The presented concept enables mode-selective FRF-based SHM systems to work with multimodal excitation, which reduces setup complexity and increases the application range by providing information for both fundamental modes over a wide frequency range.
Phononic Crystal Waveguides for Structural Health Monitoring Applications
This paper reported applications of 3D printed acrylic phononic crystal (PC) waveguide transducers for application within NEWS methods. Experimental evidence has shown to support the mathematical formulation of the imaginary component of the complex wavenumber shift used in the design of circular PCs with sinusoidally corrugated profiles. Experimental evidence has also supported the existence of band gaps resultant from the destructive interference of both incident and reflected fundamental Lamb waves at specific frequency band gaps. These stop bands allow PC transducers to possess their unique feature of natural filters undesired frequencies, for example those originated from the ultrasonic equipment. NEWS tests have finally shown that second harmonic waves generated from the contact surfaces of micro-cracks could be accurately detected with PC waveguide transducers used during ultrasonic transmission. This technology can be applied, in future, for more accurate, automatable, and simpler microcrack detection.
Damage Detection of Rectangular Sections by 2D Curvelet, Contourlet and Wavelet Packet Transform
In this research, the application of 2D curvelet, 2D wavelet, and Contourlet transform for damage detection of shell structure are presented. For this study, the equation of discrete curvelet transforms using unequally-spaced fast Fourier and wrapping transforms are employed. Four scenario models with one and more damages with different percentages and locations were considered for investigation of proposed methods. As real data has noise, and the existence of noise in the input data may affect the accuracy of damage detection methods. To consider the natural conditions, white noise has been applied to the data extracted from the FEM. First, the coefficients of transforms related to the noisy signal are calculated, then thresholding the previous coefficients calculated, and finally, the de-noise signal is reconstructed. Based on the presented results, the curvelet transform better identifies the location of damage than the wavelet transforms. Wavelet transform based on the direction of the damage element may be specified in some subbands. But in curvelet and Contourlet, it can better identify different damages in different directions due to the multi-directional transform. The curvelet transform has been modified based on polar space and poses challenges for non-polar (normal) discrete space. Therefore, using the Contourlet is divided into multiple directions because of each of the available general directions. According to the obtained results, the proposed methods are sensitive to the location and percentage of damage and can identify the location of damage with a low percentage of damage. Also, to confirm the results of the proposed methods, the results are evaluated using laboratory results. The results show that the proposed methods can identify the location of damages and with a percentage of damage of up to 3%.
Case Study for Using Open-Source UAV-Deployable Wireless Sensor Nodes for Modal-Based Monitoring of Civil Infrastructure
Experimental modal analysis is an important technique used in structural health monitoring to evaluate the dynamic properties of a structure, such as natural frequencies, damping ratios, and mode shapes. There has been an increasing interest in using uncrewed aerial vehicles (UAVs) to perform experimental modal analysis, as they offer several advantages over traditional hardwired sensing systems. UAVs equipped with deployable wireless sensor packages can capture a vast amount of data due to their high mobility, enabling the identification of subtle structural behaviors that would be more challenging to obtain using conventional sensors. This case study focuses on the use of UAV-deployable wireless sensor nodes to perform experimental modal analysis on a pedestrian bridge in use. The objective of this study is to demonstrate the potential for monitoring the dynamic properties of a structure using networks of UAV-deployable sensor packages. The paper reports on the development of an open-source UAV-deployable sensor node designed for autonomous deployment on steel structures. The open-source sensor package is a standalone system that includes independent energy storage along with nonvolatile memory and radio frequency communication capabilities. A high-performance microcontroller is utilized to record and process acceleration data in real-time. Moreover, the microcontroller processes various input/output commands, manages the transmission of status updates, and controls the electropermanent magnet onboard during the docking procedure in combination with the UAV to ensure safe deployment. The case study consists of an experimental approach, using a vibration-based sensing network in conjunction with a structural shaker, to capture the structural dynamics of a pedestrian bridge under forced excitation. The results showed that the UAV-deployable wireless sensor nodes were able to capture accurate and reliable data, enabling the identification of the bridge’s first flexural mode shape. Furthermore, this study provides valuable insights into the challenges and prospects associated with using UAV-deployable sensors for the dynamic modeling of structures in a structural health monitoring framework.
Versatile Sensor Node with Acoustic Data Communication Capabilities for FSAT Networks in Guided Wave-based Structural Health Monitoring Applications
Correct evaluation of structural integrity and the ability to predict the remaining useful life of structures is of critical importance in numerous applications, particularly for aerospace industry. The development of Structural Health Monitoring (SHM) systems and their integration into real-world structures has become a necessity to provide a robust and cost-effective solution for structural assessment. Normally structure inspection is done periodically after weeks or months. For machines such as aircrafts or trains, they are taken out of service for structural integrity evaluation. Indeed, one main advantage of modern SHM systems is the continuous, autonomous monitoring of structures, even during service. Therefore, dense networks of ultrasonic transducers are distributed over structures to perform reliable damage detection and localization. Unfortunately, powering these poses many challenges in terms of cabling and added weight, besides to long latency times if a single central processing unit is charged of managing all transducers in the network. To solve this issue, new systems rely on specialized sensor nodes (SNs) and transducers, such as frequency steerable acoustic transducers (FSATs), which offer inherent directional capabilities for data actuation/ sensing. These SNs can drive high voltages and various types of signals to and from transducers conforming a SHM network. They also perform signal treatment tasks and lead communication protocols, such as on-off keying (OOK) or frequency shift keying (FSK), and even help identifying defective nodes within the network. Given the specificities of the FSATs, a matching SN is required to ensure reliable data exchange, even in cases in which traditional radiofrequency-based wireless communication is not possible. In this contribution, we address this need by presenting the design, implementation and test of a new, lightweight and low-cost SN, capable of driving voltage signals as high as 60 Vpp and powering both: commonly used piezoelectric transducers and the more specialized FSATs, and perform communication tasks, using a regular 5V USB port as power source. With this SN, the weight, latency and cable complexity of regular sensor networks are addressed, while disclosing novel opportunities for a higher level of automation and, hence, safer embedded SHM systems for ultrasonic inspection.
Heterogeneous Sensor Placement Under Uncertainty
This study presents a heterogeneous sensor placement optimization framework using deep reinforcement learning (DRL) that considers system parameter uncertainty. The sensor placement problem is a well-established combinatorial optimization problem characterized by inherent parameter uncertainties that affect system responses that sensors measure. These uncertainties render deterministic solutions insufficient and necessitate a computationally tractable approach to account for the uncertainties. The proposed method incorporates a Markov decision process (MDP) as a stochastic environment, and a sensor placement agent trained using DRL. The agent’s objective is to maximize the effectiveness of sensor placement within a system by selecting sensor types and locations. The agent’s sequential decision-making is guided by a reward function that is designed based on the observability Gramian, calculated using sampled parameter values from an a priori distribution. The proposed approach is validated through simulation of a case study involving heterogeneous sensors in a shear building model with results compared to those from an evolutionary algorithm. The results show that the sensors selected by DRL method match the CMA-ES algorithm with the advantage of having information about relative importance of the selected sensors.
A Real-Time Framework for Structural Health Monitoring Based on the Internet of Things—An Experimental Study
Structural Health Monitoring (SHM) is a continuous and autonomous monitoring technique for ensuring structural safety, integrity, and performance without affecting the structure itself. Advances in technology help in identifying structural defects early on, which aids in making decisions pertaining to when repair and rehabilitation are required for civil structures resulting in saving the economy. The Internet of Things (IoT) and cloud services paradigm extends a faster and more reliable SHM strategy. There have been few studies, especially in data driven SHM for a while, where SHM with IoT was implemented over traditional monitoring techniques. However, solutions presented in the recent literature have the following drawbacks viz, latency, power consumption, and real-time synchronization. Most approaches fail to meet real-time requirements, resulting in slower data visualization, retrieval, and analysis, which might lead to late warning alarms eventually leading to catastrophic events. The present research study focuses on these problems by building a completely feasible, cost-efficient, and scalable IoT architecture utilizing AWS cloud-based services with real-time constraints. Data acquired from the sensor nodes are transmitted via a communication module following star topology to the fog layer (DAQ) through MQTT to the AWS cloud and is processed through different cloud services and is visualized immediately making it an asynchronous architecture. Also, the paper presents the tradeoffs for choosing sensors, communication protocols, algorithms, and cloud services for an efficient and secure real-time approach with an aim to provide a promising platform for an efficient and proactive SHM system for civil infrastructures. Our architecture has been tested with Commercially Off-The- Shelf (COTS) hardware on the prototype models developed in the lab and the results have been validated for the same. The extension of this research work for real-time applications has also been discussed.
Sensor Location Optimization for Efficient Structural Health Monitoring Through Intelligent Design
Vibration based Structural health monitoring (SHM) is an emerging field in Civil engineering, which evaluates the structure’s integrity and performance. It facilitates the early identification of damage/deterioration and reduces maintenance costs. Due to technological development in research nowadays, sensors play a critical role in SHM, by offering consistent and reliable measurements of structural dynamic parameters, such as displacement, temperature, strain, and vibration, thereby enabling timely detection of potential issues, targeted maintenance, and repairs. However, choosing the correct sensor locations on a structure can be ambiguous, hence, the development of an effective sensor optimization technique is essential so that it can reduce the maintenance cost. This involves identifying the most relevant parameters to monitor and determining the optimal number and location of sensors. Too many sensors can result in excessive data and processing requirements, leading to increased costs and reduced reliability. Conversely, too few sensors can result in incomplete data and reduced accuracy, leading to missed or delayed detection of potential problems. Hence, sensor optimization is necessary to ensure optimal data acquisition and efficient monitoring. The algorithm proposed in this study for sensor optimization employs Particle Swarm Optimization (PSO), Bayesian Optimization (BO), and Optuna to determine the optimal sensor locations for monitoring a structure. To achieve this, the algorithm employs modal assurance criteria (MAC), Fisher Information Matrix (FIM), and both MAC and FIM as objective functions for each of the optimization algorithms in addressing the Optimal Sensor Location Problem (OSLP). The algorithm aims to provide an efficient and effective method for determining the best sensor locations for monitoring the structure’s health and identifying system parameters, such as frequencies, mode shapes, and damping ratios. The algorithm aims to provide a reliable method for identifying system parameters such as mode shapes, frequencies, and damping ratios by finding the best sensor locations. By leveraging the strengths of PSO, BO, and Optuna, the algorithm optimizes the objective function to identify the best sensor locations for the structure and also gives minimum and maximum number of sensor locations for identifying the system parameters. Based on the optimized results obtained from utilizing the optimization techniques in the OSLP, it can be concluded that the optimal sensor locations can guarantee improved linear independence of the MSV and are validated with experimental modal parameters. Furthermore, the proposed algorithm can provide a reliable and efficient method for determining the best sensor locations to monitor a structure. Such findings can greatly benefit the field of structural health monitoring and contribute to enhanced safety and reliability of engineering structures.
A Mobile Sensor Network System for Sound and Vibration
The smartphone has become a powerful computing device and is equipped with various sensors due to the advancement of sensor technology. In this study, we investigated whether the built-in accelerometer sensor of a smartphone can replace the traditional SHM measuring device, an accelerometer sensor. To compare and analyze the performance, we conducted a modal analysis using a small-sized PCB accelerometer and compared it with the traditional device on a steel frame specimen.
The Road to an Open and Secure Data Exchange Infrastructure for SHM
Today, structural health monitoring systems consist of various hardware and software components that are not always well synchronized. The paper emphasizes the need and challenges to synchronize multi time-domain and multi physical-phenomena data acquisition systems. On the way from a raw data sensor system to an independent decision-making edge device, there are still some challenges to be solved along the way. Identifying and combining technologies from foreign domains is the focus of the journey. Open and standardized communication protocols like OPC UA or MQTT are already wellestablished technologies with a high level of security. These communication protocols allow users to discover and receive structured data from compatible sensors and edge devices in the network. Moving the data processing and decision-making further to edge leads to reduced data transfer volume, lower latencies and improved privacy, security, and cost-effectiveness. This requires an extensible platform to embed custom feature extraction algorithms and to deploy trained machine learning models. ONNX, the open standard for machine learning interoperability, is a further puzzle piece on this journey. The paper shows the status of work, ongoing developments in the open standard community like OpenAE and platform strategies for advanced analytics and AI.
Unsupervised Vehicle Classification Using a Structural Health Monitoring System
The structural performance assessment of bridges is a crucial issue for managing transportation infrastructure systems in EU countries as traffic loads and structural ageing continues to increase. Weight-in-Motion (WiM) systems have been developed to estimate the gross weight of vehicles over a bridge and keep the bridge load under control. However, WiM systems are costly in procurement and installation; alternative approaches that aim to be more scalable and cost-effective are needed to respond to the need to monitor large-scale infrastructures. This work explores an innovative zeroincremental cost approach based on raw vibration data extracted from a system already deployed for Structural Health Monitoring (SHM) and based on MEMS accelerometers. A novel signal processing and classification pipeline has been developed to differentiate vehicles into three categories: light, i.e., less than 10 tons; heavy, i.e., between 10 and 30 tons; and super heavy, i.e., above 30 tons, using only features extracted from vibration data. The results show that this framework can distinguish vehicles with an accuracy of 96.87%, utilizing the mean-shift unsupervised clustering model. This method has the potential to be a significantly cost-effective and scalable solution for monitoring bridge loads compared to WiM systems, as it leverages existing SHM infrastructure and affordable MEMS sensors to provide real-time information on vehicular loads.
Exploring Crowdsensing Potentials for Structural Health Monitoring Applications: Challenges and Opportunities
The use of devices equipped with sensing capabilities (ranging from smartphones to smartwatches) has become commonplace in our daily lives. These devices can measure various parameters such as acceleration and position, and have the potential to be leveraged for purposes beyond their intended use. This is the main goal of crowdsensing techniques. However, if on the one hand crowdsensing is spreading for its great appeal, on the other hand sometimes the importance of key concepts related to the robustness of the measurement data collected are underestimated. This is particularly true when crowdsensing techniques target structural dynamic problems. Indeed, the accurate extraction of dynamic information beyond the resonant frequencies of the structure is still an open issue and requires tackling the problem considering metrological aspects. Including effects like different sensitivities, sampling frequencies of the devices, as well as all the other interfering inputs that might affect a structural dynamic measurement, will provide the basis for extracting enriched structural data like mode shapes and damping. This may pave the way to a paradigm shift in monitoring civil infrastructures, as fixed installations could be correlated, compensated, or even substituted by mobile solutions. To prove these potentials, this paper discusses the main challenges arising in this application field of crowdsensing, such as the need of synchronizing the devices if targeting operational deflection shapes (ODSs) reconstructions, the lower sensitivity of sensors incorporated in mobile/wearable devices (typically based on micro electro-mechanical system – MEMS – technology), etc. These issues are demonstrated and tackled by performing a three-step analysis. At first, the metrological characterization of the selected hardware is carried out. In the second step, a first experiment is conducted on a laboratory case study, to verify whether the proposed system can capture the dynamic response of the structure. The final step consists of an experimental campaign conducted on a pedestrian bridge. The results obtained clearly demonstrate the need for a multidisciplinary approach which includes metrology science when targeting crowdsensing for the assessment of the dynamic behavior of a structure.
Certification of CVM™ Sensors for Monitoring 737 Aft Pressure Bulkhead
Structural health monitoring (SHM) systems are a desired solution to provide aircraft operators information on the health of aircraft structures. The Federal Aviation Administration (FAA) is responsible for ensuring the safety of the air transportation system in the United States and its certification of SHM systems is essential to ensure that these systems meet safety standards and do not compromise aircraft safety. This paper provides an overview of the efforts undertaken to supply the necessary data and analysis for certification of a CVM SHM system, including the regulatory requirements and the steps involved in the certification process. Additionally, this paper discusses the benefits of SHM systems for the aviation industry and their potential impact on safety. Cost and time savings are driving the demand for certification of a CVMTM SHM system that satisfies the requirements of Boeing SB-737-53A1248 along with the guidance of an FAA Issue Paper. This certification would be the first for any SHM system in a safety critical Principle Structural Element of a Commercial Fixed Wing Aircraft, the Aft Pressure Bulkhead (APB), where an FAA Airworthiness Directive is mandating the inspection for 737 operators. The existing Service Bulletin allows for two inspection options, Option 1: LFEC and detailed inspection (aft side) every 1,200 flight cycles or Option 2: HFEC and detailed inspection (fwd side) every 3,800 flight cycles. The approval of the revised service bulletin would allow for Option 3: CVMTM inspection (fwd side) every 1,200 flight cycles, thus reducing the inspection time from 24 hr to 15 min1.
An Original Smart Data Sampling for Wireless Sensor: Application to Bridge Cable Monitoring
Structural health monitoring (SHM) systems often ask for a robust, flexible and costeffective solution. In that domain, since years, the technological development of Wireless Sensor Network try to be an answer. Between many other questions, one of the keypoint in wireless sensing resides in the time synchronization (e.g. how to ensure the same time base between electronic systems that doesn’t know each others ?). At Gustave Eiffel University, robust and deterministic solutions based on GNSS modules have already been demonstrated [1], the goal of the work presented in this paper is to go deeper into turn-key solutions by implementing and coupling this GNSS-synchronization principle into a low-power FPGA to an Analog-To-Digital converter. This hardware and software association represents a generic solution for signal sampling in a wireless manner. This work is illustrated and demonstrated by an application on the acoustic monitoring of wire-breaks in bridges cables.
Implementation and Validation of a Low-Cost loT-Enabled Shake Table System
Shake tables are essential tools for validating structural health monitoring (SHM) systems. However, commercially available shake tables are relatively expensive and often hardly affordable for research and teaching institutions. Moreover, the widespread adoption of remote working and online teaching, fostered by the COVID-19 pandemic, often prevents researchers and students from physically accessing laboratories to conduct experiments. Research and teaching institutions would benefit from using lowcost shake tables with Internet-of-Things (IoT) capabilities for remotely conducting shake table experiments on test structures equipped with SHM systems. This paper presents the implementation and validation of a low-cost IoT-enabled shake table system (IoT-STS). The IoT-STS is composed of off-the-shelf components and builds upon a four-layer architecture. A smartphone application is devised to send periodic or random excitations, i.e. displacement time histories, through an IoT-based cloud service to a microprocessor, which, via actuators, imposes excitations on the shake table. Vice versa, the microprocessor sends the output displacement data, representing the motion reproduced by the shake table, to the cloud for remote access and visualization. To validate the IoT-STS, displacement time histories, remotely entered through the smartphone application, are compared with output displacement data, recorded by a potentiometer. The results demonstrate that the low-cost IoT-STS is capable of accurately imposing periodic and random excitations on test structures, thus showcasing the compatibility of the IoT-STS with remote working and online teaching in light of the COVID-19 pandemic.
Adapting Mach-Zehnder Interferometers for Vibration Sensing
Silicon photonic sensors are superior alternatives to traditional electromechanical sensors in applications such as vibration monitoring of data center buildings or real-time seismic wave detection at underwater fiber optic network sites. Because they communicate using optical signals, these sensors can be seamlessly integrated into a data center’s existing architecture, or underwater fiber optic networks. Their immunity to electromagnetic field interference and ability to withstand harsh environments also contribute to the suitability of these optical sensors for implementation in the nuclear energy industry and power management facilities. The Mach-Zehnder Interferometer (MZI) is an optical component that operates based on the constructive and destructive interference of light passing through its two arms. This component is usually used for electro-optical modulation in telecommunication. By allowing one of its arms to vibrate freely on a silicon chip, the MZI can also function as an accelerometer. In an MZI accelerometer, the output light power and free spectral range (FSR) correlate with applied vibration. Only, measuring the output power of an MZI at its optical resonance frequencies and/or measuring its FSR in real-time is a challenging task. We propose two modifications to resolve this issue. The first is to create an imbalance to the MZI by increasing the fixed arm length. The resulting MZI responds to applied vibration through changes in the FSR, which can be easily measured using peak-detection. The second modification involves adding a heater as an optical modulator on the fixed arm. A closed loop feedback controller can then be used to apply enough power (heat) to compensate for the vibration of the freestanding arm. The magnitude of applied power correlates with the acceleration. The proposed modifications to the MZI will enable more straightforward and efficient measurement of acceleration using silicon photonic sensors, making them an even more versatile option for a wider range of applications.
High-Velocity Impact Detection and Localization Enabled by 10 MSa/s Sample- Rate FBG Interrogation
Real-time structural health monitoring (SHM) systems for airborne structures can help detect and predict damage to critical components. This study demonstrates a system for real-time detection and evaluation of high-velocity foreign object impacts on composite structures using fiber Bragg gratings (FBGs). A set of fiber Bragg gratings were applied to a composite helicopter blade, and by using high-speed and wide frequency-band FBG interrogation, we demonstrate object impact detection made by projectiles of various materials and weights, with speeds from 100 m/s up to 200 m/s and their respective elastic wave propagation along the blade. Early impact damage detection has the potential to reduce the chance of critical failure and decrease aircraft downtime caused by rigorous and time-consuming non-destructive inspections.
High Sensitivity Wireless Strain Sensor for SHM Applications
Our research aims to develop a new generation of ultrasensitive strain sensors with wireless communication of data and energy, low power consumption, and easy installation within structures to be used as in-situ measurement systems. The sensor has been developed based on RFID sensing technology that allows wireless data and power transmission by inductive coupling between the internal inductance of the sensor and the external readout coil. Microfabrication technology is used to fabricate the sensor by patterning a metallic LC circuit on a flexible substrate. Nano cracks are introduced to the electrode to create a piezoresistive effect that leads to a transmission line behavior of the capacitance electrodes. The sensor has been embedded with GFRP and CFRP, a bending test has been performed, and the sensor is used to measure the strain during the test. The sensor proves its ability to detect small strains in the composite structures due to the unconventional change in capacitance of the LC oscillator. This unconventional change in capacitance results in a large shift in resonance frequency, producing a sensitive wireless strain sensor with a Gauge factor of 50 for less than 1% strain.
Lamb Wave Actuation and Propagation Using Flexoelectric Transducers
In recent years, actuation and propagation using a relatively novel electromechanical effect, namely flexoelectricity, have become an emerging field of research. In this article, we explore the use of flexoelectric dielectrics for Lamb wave actuation and propagation for structural health monitoring (SHM) applications. Towards this objective, an analytical solution is presented for Lamb wave generation and propagation in plate-like structures using flexoelectric-piezoelectric transducers bonded to the host plate surface through an adhesive layer. The model considers both interfacial shear and peel stresses at the interface of the transducer and the plate. These interfacial stresses under applied electric actuation are determined based on a one-dimensional (1D) model, which assumes Kirchhoff plate-like behaviour for both the actuator and the host plate. A 2D elasticity solution is obtained for Lamb wave propagation in the host plate under the interfacial shear and peel stresses obtained from the above 1D model considering plane strain conditions. This solution is obtained in the wave number domain by applying Fourier transform spatially. Finally, the response is obtained in the physical domain by applying the inverse Fourier transform and residue theorem. The strain response at the plate surface is compared with the rigid bonding model to illustrate the effect of bonding compliance. The numerical study demonstrates the effect of the adhesive layer on the response for different excitation frequencies.
Fluorescence-Based Fiber Optic Sulphate Sensor for Long Term Sulphate Ion Monitoring in Concrete Structures
Concrete has been one of the most popular construction materials around the world. However, sulphate ion will cause of concrete degradation and lead to the decrease of service life of concrete structures. The formation of ettringite and other reaction products can lead to the decrease of service life of concrete structures, thus, a continuous monitoring of sulphate ions is needed as a reference for the maintenance work of concrete structures. This paper presents the design and development of a fluorescence-based fiber optic sulphate sensor for this purpose. The sensor's design capitalizes on the changes in fluorescent intensity of morin-metal ion complexes induced by sulphate concentration changes. By using cellulose acetate, the complex is transformed from a solution to a thin film, serving as the sensing element in the sensor probe. The sensor provides a response over a sulphate molarity range of 0 mol/l to 0.010 mol/l. And after being embedded inside the cement paste, the sensor still responds to sulphate. These features make this fluorescence-based fiber optic sulphate sensor potentially suited for in field and long-term monitoring of sulphate ions in the concrete structures, thereby enhancing structural health monitoring within the civil engineering sector.
Validation of a Piezo Based Integrated Process and SHM System on Hollow Composite Aircraft Part Made by Vacuum Infusion Using a 3D Printed Smart Mold
During the last years the authors developed a novel hybrid multi-functional piezo / temperature sensor concept for flow front, cure and structural health monitoring of composite parts thus allowing monitoring of the production and the usage with the same sensor type. Within this paper, the authors present the latest validation results of the developed sensor and monitoring concept on a hollow composite aircraft part. The production of the part was done by vacuum infusion using a smart separable Al mold, made by selective laser melting to enable surface near complex cooling channels and lightweight design. Process parameters such as flow front propagation and degree of cure were successfully monitored during the production of the part. Finally, the ability of the co-cured sensor for passive acoustic emission monitoring for the occurrence of and localization of impacts and the application of guided ultrasonic waves was demonstrated.
Monitoring Damage Evolution in Carbon/ Epoxy and Carbon/Thermoplastic Composites using Acoustic Emission Technique
Detecting, monitoring, and quantifying the growth of damage in structural components in real time is important for assuring safety of aerospace structures. Acoustic emission (AE) technique is one of the tools that can facilitate such monitoring and quantification of damage growth and provide meaningful insight into damage evolution. Damage growth in composite materials has long been studied under laboratory setting and documented in the literature, but there is a need to track such failure modes in real structures under operational conditions. In this study the progression of damage in pristine carbon/epoxy composites under static loading were examined. Bonded wide band sensors capable of detecting frequency components up to 2 MHz were used to differentiate signals from the three primary AE sources, namely, matrix cracks, delaminations, and fiber breaks. Traditional acoustic emission parameters as well as the waveform characteristics were used to classify the acoustic emission signals related to the three failure modes. Individual clusters of fiber breaks, progressive growth of individual matrix cracks as well as delamination growths were also traced using these techniques. The correspondence between the failure mode and the respective waveforms characteristics were validated to a limited extent using both experimental techniques as well as numerical simulations. Based on these classifications, the rate of growth of individual failure modes was also quantified using their respective cumulative energy. The appearance of greater number of AE clusters related to fiber breaks and the increase in their sizes along with cumulative energy are found to be a clear indication of impending failure.
A Comparison of Optical Sensing Systems with Piezo-Electric Sensors for Impact Identification of Composite Plates
This paper proposes using Fibre Bragg Grating (FBG) and piezoelectric (PZT) sensors for impact identification. The main objective is to evaluate the ability of the state-ofthe- art sensors to estimate the impact energy, using the results of the PZTs as a reference. This approach allows a fair comparison and overcomes the inherent variability of different test runs of the same measurement. The comparison of sensor technologies consists of evaluating sensitivity to features for impact energy estimation, signal strength, repeatability, directivity, and signal correlation. Small-mass impacts were applied to a square composite plate at different locations and energies. The energy was kept low enough to avoid damaging the panel. The PZT and FGB sensors were placed at the same locations but on either side of the panel to compare signals evenly. The results showed that the energy from the measured response reflects the impact energy level. Moreover, FBGs and PZTs had comparable responses and an apparent similarity in time response, besides consistency in the frequency domain. The higher sampling rate for the PZTs allows for the analysis of higher frequency bands, compared to FBGs, showing relevant amplitudes above 10kHz. Future work will focus on developing and validating a force reconstruction algorithm and defining the optimal sensor configuration.
Computational Modeling of High-Temperature MEMS Sensor Array for Ultrasonics and Acoustic Emission in Structural Health Monitoring of High Temperature Advanced Reactor Pipes
In recent years, development of advanced liquid metal thermal hydraulics enabling technologies has become increasingly important for commercial deployment of advanced reactors. One of the challenges is the need for reliable structural health monitoring (SHM) systems capable of detecting early signs of critical metallic structure failure in high-temperature environments. To address this need, we investigate a new type of piezoelectric microelectromechanical system (MEMS) sensor that can continuously operate at high temperatures. We study the use of a circumferential array of high-temperature piezoelectric MEMS sensors for deployment on a piping system of Argonne’s Mechanisms Engineering Test Loop (METL) liquid sodium thermal hydraulic facility. The MEMS sensors consist of square silicon carbide wafer with 5 mm length and aluminum nitride as piezoelectric element. These materials were shown to be resilient to ionizing nuclear radiation in prior studies. The sensors are arranged in a circumferential array on the pipe surface to enable dual ultrasonics and acoustic emission sensing. To determine the optimal number of transducers needed to achieve the excitation of Lamb wave for ultrasonics testing, we created a Multiphysics numerical model to simulate the coupling between the sensors and the pipe. Overall, the results of the study demonstrated the potential of using high-temperature piezoelectric MEMS sensors for SHM in liquid metal reactors. By detecting critical structure failure in high-temperature environments, these sensors could help reduce the risk of forced reactor shutdown and minimize operation and maintenance costs.
Combined Acoustic and Modal Structural Health Monitoring and Structural Assessment
The paper presents the first results of a recently completed R+D project KamoS - the acronym stands for Combined Acoustic and Modal Structural Health Monitoring and Structural Assessment. The scope of the project was to understand how combined approaches based on guided ultrasonic waves, acoustic emission and vibration measurement can be used for the monitoring of aircraft structures under changing operational conditions. The main idea is to combine the advantages of local and global Structural Health Monitoring (SHM) approaches. For the experimental validation of the first developed algorithms an extended measurement campaign was carried out on a realistic aircraft door environment. The laboratory tests combined active and passive measurement principles based on ultrasound and vibration to ensure robust and reliable damage detection and localization. In this paper only the experimental setup of the aircraft structure and some results based on vibration monitoring are presented. The obtained measurement data sets serve for the development of further damage identification algorithms.
Semi-supervised Learning for Acoustic Vision Monitoring of Tendons in Pre-stressed Concrete Bridges
Aging bridge infrastructure appears to become a major challenge in many industrialized countries. Numerous bridges are in bad condition and the current pace of repair and replacement as well as the available financial resources hence demand for a reliable bridge monitoring to facilitate an extended operation period of existing bridges. Nowadays, prestressed concrete bridges are prevalent among other construction types but may suffer from stress corrosion cracking of steel tendons. To detect wire breaks in bridge tendons, recent research suggests the use of acoustic emission analysis. In this work, we propose the use of semi-supervised learning techniques for anomaly detection to detect wire breaks in tendons of prestressed concrete bridges. Particularly, we utilize only acoustic emissions due to traffic and other environmental influences, recorded on a real bridge in operation, to initialize the local outlier factor algorithm. We then apply the initialized local outlier factor algorithm to two separate datasets with more than 500 wire break signals recorded on two different types of bridge girders. It is shown that the anomaly-based approach outperforms a supervised k-nearest neighbors classifier trained using wire breaks from only one girder. An evaluation on the wire break signals from the second bridge girder, not seen during the training phase, shows an improvement of the average recall score from 38 % to more than 99 % for the anomaly-based approach compared to the supervised k-nearest neighbors classifier. Considering the diversity of bridge constructions and the fact that availability of acoustic emission signals due to wire breaks is limited, semi-supervised learning seems to be a suitable approach for wire break detection. Furthermore, acoustic emissions due to normal environmental and operational conditions could be easily and cost-effectively recorded during an initialization phase of any monitoring system and thus be utilized to initialize an anomaly detector for each specific infrastructure.
Non-Contact Acoustic Emission Monitoring of Corrosion-Fatigue Damage in Submerged Steel Structures
Corrosion-fatigue is considered one of the main degradation mechanisms affecting the structural integrity of offshore support structures and mooring systems. These structures are typically covered with marine growth of various types. This paper highlights a feasibility assessment of detection and localization of corrosion-fatigue damage in submerged steel structures using non-contact Acoustic Emission (AE) technique. An accelerated corrosion-fatigue experiment was conducted on a dog-bone steel specimen. A dedicated experimental set-up was designed to simultaneously apply accelerated corrosion and cyclic loading on the dog-bone steel specimen submerged in artificial seawater. Ultrasound signals were continuously measured using an array of underwater AE transducers (in the frequency range between 50-450 kHz) placed at a fixed distance from the tested coupon. A source localization algorithm for corrosionfatigue- induced ultrasound signals was successfully implemented. The results of the accelerated corrosion-fatigue experiment suggest that corrosion-fatigue-induced ultrasound signals can be detected with a satisfactory signal-to-noise ratio using noncontact AE transducers. Four stages of corrosion-fatigue damage growth were identified which may be used as the basis for possible damage characterization.
Automatic and High-Precision Acoustic Emission-Based Structural Health Monitoring of Concrete Structures
Acoustic emission (AE) monitoring is a useful technique to monitor the health of a structure continuously, helping to prevent potential failure. AE are elastic waves produced and emitted during fracture processes inside a material and are recorded by sensors. Using quantitative geophysics-based methods, the recorded signals can be processed to monitor and describe the spatio-temporal growth of fracture in brittle materials such as concrete in real-time. Because of the complex nature of the recorded elastic signals and the non-homogeneous medium condition of concrete, data are usually processed manually. Combined with the high processing cost of the large datasets collected, which may exceed Terabytes, this approach has not found many real-world applications. Thus, an automated methodology is needed that can reduce costs, while maintaining high-precision, for implementation in a structural health monitoring (SHM) scheme. Here we discuss the application of a new automated and high-precision AE monitoring algorithm and software called SIMRGH [5,6] suitable for SHM of concrete structures. The core software has been developed for the laboratory-scale (in scale of centimeters) hydraulic fracture monitoring. It is up-scaled to the meters scale and works for heterogeneous media. The software works with various standard data formats and can handle trigger-based as well as continuous data. In this paper, we show some initial results of implementing the software for AE monitoring of two 4.88-meter-long concrete beams loaded in the laboratory and compare it with manually processed AE data. We were able to locate at least three up to 10 times more AE sources compared to when manual processing was used and with higher precision. If enough processing units are provided, the software can run in parallel and enable real-time SHM with excellent precision on crack geometry imaging. Future work will include implementing moment tensor inversion (MTI) to characterize AE source physics, providing valuable information for decision makers regarding the nature of the captured data.
Acoustic Emission Monitoring of Pre-Stressed Concrete Beams During Accelerated Corrosion of Pre-Stressing Tendons
Acoustic sensors attached to pre-stressed/post-tensioned bridges are promising tools for monitoring the progression of corrosion damage in pre-stressing/post-tensioning tendons. In this study, acoustic emission signals from a pre-stressed beam containing three pre-stressed tendons that were exposed to accelerated corrosion conditions were studied. A short length of each tendon was exposed, and a tank filled with the NaCl solution was placed over the exposed tendon. Over a period of several months, a corrosion current was driven into the tendons until at least one wire corroded through. Acoustic sensors were attached along the beam and were used to record acoustic emission events during the accelerated corrosion. At the termination of the accelerated corrosion experiment, the beam was sliced into sixty-two cross-sections, each being 5 cm thick. Each slice was inspected to correlate corrosion and tendon slippage with acoustic emission signals. Maps of the estimated origin of acoustic emission signals were compared with the maps of the position of tendon corrosion and slippage. The acoustic emission signals were correlated with the presence of wire fracture due to corrosion on the tendon and with proximity to the end of the beam. The larger emission signals are likely due to the loss of bond between the tendons and concrete, as tendon fracture due to corrosion was not found in any of the cross-sections. This work points to the use of acoustic emission to track the progression of damage in cases where corrosion has already resulted in tendon fracture, and progression is proceeding by loss of bond.
Acoustic Emission Source Location Using Bayesian Optimisation for a Composite Helicopter Blade
It has been demonstrated that acoustic-emission (AE) based inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. These advantages have resulted in the use of AE-based systems in Structural Health Monitoring (SHM) applications becoming an increasingly popular research topic. The strong correlation between features of AE-waves and the sources that generate them can be exploited to reveal information of the health-state of a structure in a non-invasive framework. This idea has been demonstrated successfully in the early detection of crackgrowth, nucleation, corrosion, dislocation slips, and cavitation, among others. There are, however, numerous challenges associated with the analysis of AE-data. Some of these challenges include sensor placement, calibration, operational variations, sources of noise, and manipulation of large-size datasets. These challenges must be addressed carefully if one wishes to implement an AE-based system in SHM applications. Although the analysis of AE-data can be complex, its advantages can be meaningful in the development of an optimised maintenance schedule, reducing costs associated with unnecessary repairs, or by preventing potentially catastrophic outcomes from overlooking an emerging defect that can be detrimental to the safe operation of the structure. In this paper, AE data recorded from a full-scale helicopter blade are processed to develop a probabilistic model designed to predict the location of potential sources of damage. The following work addresses some of the challenges related to the construction of ΔT maps and proposes a novel strategy for identifying optimal sampling points, eliminating the need for extensive data collection for training.
Acoustic Emission Source Localization in Fiber-Reinforced Composites based on Multimodal Dispersion Compensation of Guided Waves
Fiber-reinforced composite materials are widely used in the aviation, civil, and shipbuilding industries. Especially the latter two industries are typically dealing with thicker composites. At the same time, in these industries the need for structural health monitoring, to assess degradation and failure, is becoming more prevalent. Acoustic emission (AE) measurement and analysis for damage source localization and characterization can be a useful method for the assessment of structural integrity for these structures. In the case of composite panels, acoustic emissions can propagate in the form of elastic guided waves. The location of the AE source exposes regions in a structure that are subject to degradation. Typical acoustic emission source localization methods assume that the recorded AE signals consist of a single dominant fundamental wave mode. However, with thicker composites, the acoustic emissions may propagate in a multitude of modes. This will complicate the signal processing operations for accurate source localization. This research assesses experimentally how guided wave multimodality influences acoustic emission localization. An acoustic emission source is excited in a thick glass fiber-reinforced plastic (GFRP) panel. Measurements from this excitation are first assessed for their content of higher modes. Source localization is carried out based on dispersion compensation through time-distance domain migration. Different possibilities and combinations of wave modes are considered. The localization error is assessed for each option. The results highlight the added complexity of multimodality and show how the inclusion of multiple modes into the procedure can improve the accuracy of source localization.
Applicability of Data Augmentation Through Variational Autoencoder for Two-Dimensional Acoustic Emission Source Discrimination on Hollow Cylindrical Structures
A data augmentation method is studied in this work to supplement previously proposed variational autoencoder (VAE) based acoustic emission (AE) source discrimination tool. The VAE model distinguishes the sources of collected AE signals through the multiple Lamb mode arrivals resulting from helical paths. It displayed the ability to discriminate pencil-lead-break (PLB) waveform dataset collected at a liquid nitrogen tank by source locations. VAE infers source-discriminative latent variable distribution conditioned on the observed waveforms and can be further applied to localization predictions for unseen waveforms. However, the prediction will be limited to source coordinates included in the training dataset. Therefore, this work studies the applicability of data augmentation using VAE to approximate waveform envelopes from sources coordinates that are not included in the PLB dataset, which are called target source in this study. Approximated waveforms of target source were introduced by interpolating the learned mode arrival characteristics to augment the original dataset. The augmented data set trained an updated latent variable distribution that accounted for the target source. Actual waveforms from target source were projected to updated latent space and validated the effectiveness of augment waveforms. This positive outcome provides confidence on pursuing data augmentation applications through VAE to assist AE localization.
Advanced Health Monitoring of Composite Structures Through Deep Learning-Based Analysis of Lamb Wave Data for Developing Health Indicators
A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage accumulation process during operational conditions. Additionally, designing a HI that is independent of historical SHM data (i.e., from the healthy state until the current state) is even more challenging as HI and remaining useful life prediction are time-dependent phenomena. A reliable SHM technique is required to extract informative time-independent data, and a powerful model is necessary to construct a proper HI from that data. The lamb wave (LW) technique is a useful SHM method that can extract such time-independent data. However, translating the LW data at each time step to the appropriate HI value is a challenge. AI—deep learning in this case—offers significant mathematical potential to meet this difficulty. A semi-supervised learning approach is developed to train the model using the simulated ideal HIs as the targets. The model uses the current LW data, without prior or subsequent data, to output the current HI value. Prognostic criteria are then calculated using the entire HI trajectory until the end-of-life. To validate the proposed approach, aging experiments from NASA’s prognostics data repository are used, which include composite specimens subjected to a tension-tension fatigue loading and monitored using the LW technique. The LW data is first processed using the Hilbert transform, and then, upper and lower signal envelopes in two states – baseline and current time – are used to feed the deep learning model. The results confirm the effectiveness of the proposed approach according to the prognostic criteria. The effect of different triggering frequencies of the LW system on the results is also discussed in terms of the prognostic criteria.
Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring
Under in-plane compressive load conditions, the growth of a delamination initially induced by an impact can be followed by a fast growth after a threshold level, which leads to a catastrophic failure in composite structures. To avoid reaching this critical level, it is essential to uncover the delamination size and growth pattern in real time. Ultrasonic Guided Waves (UGW) have a strong capability to interrogate and monitor the structure in real-time and thus track the growth of damage, which may occur during the flight cycles. Although various types of damage affect the monitored UGW signals, it is challenging to determine from the UGW signals what types of damage and at what rate of growth are occurring within the structure. UGW signals can be acquired at defined intervals and then analysed to possibly detect different types of damages, such as delamination, and to quantify the rate of damage growth over fatigue cycles. However, correlating the UGW-based Damage Indicators (DIs) with the specific type of damage, such as delamination, and damage growth is a challenging task as the relation between these DIs and the actual damage state is very complex. Therefore, in this study, a supervised Deep Neural Network-based (DNN) prediction model is proposed aiming to diagnose the delamination size of the composite structure by correlating the UGW-based DIs with the quantified time-varying delamination size. UGW data is collected through a network of permanently installed piezoelectric transducers (PZTs). The delamination size is obtained through ultrasonic C-Scan technique at defined cycles. DIs are extracted in time, frequency, and time-frequency domains and used as the input for the DNN-based regression model. Each sensoractuator path is considered as an independent set of indicators, which are separated for training, validation, and testing purposes. The effect of the different paths on the delamination size prediction is presented along with the model performance on measured delamination growth in woven type composite sample.
Pointwise Damage Mapping using Guided Wave Signals for Structural Health Monitoring
This paper presents a point-wise damage mapping algorithm for composite aerospace structures wherein we classify each location of interest on a structure as belonging to a damaged part of the structure or otherwise. The system employs an array of transducers attached to the structure for inducing guided waves in the structure and recording resultant waveforms arriving at the sensors. We present a baseline signal-based supervised learning algorithm in which baseline measurements on the structure are made prior to deploying it in the field and signals recorded during inspection are compared with their baseline counterparts. For each location of interest on the structure, difference metrics and other characteristics for M closest actuator-sensor paths to the location (M is typically much smaller than the total number of actuator-sensor paths on the structure.) are used as input features to a machine learning classifier. The classifier outputs for all locations of interest are further processed through a series of morphological filters to produce the damage map for the structure. The algorithm was experimentally validated on data recorded from a unidirectional composite panel impacted at five locations. Experimental results show that, for sufficiently high sensor densities, the algorithm presented in this paper is capable of detecting and characterizing multiple damages accurately and with high resolution and Sørensen-Dice index greater than 0.9 for all test cases, and is comparable to results obtained from traditional C-scan ultrasound non-destructive inspection systems. The point-wise damage mapping algorithm offers an attractive high-resolution alternative to traditional grid-based tomographic reconstruction approaches.
Meta-Transfer-Learning based Damage Detection of CFRP Composite Structures
Applying data-driven approaches in damage detection of CFRP composites is becoming increasingly popular with the rapid development of deep learning methods. However, obtaining enough data for training these data-driven models is challenging, and the presence of imbalanced data can further exacerbate the problem. Moreover, due to the limited availability of CFRP data under various structural conditions, it is desirable to make the most use of the existing data or leverage the previously learned models. To handle these problems, we propose a transfer learning-based approach, which combines the benefits of transfer learning to overcome the challenges caused by limited data, and benefits of meta training to effectively train new models. Our experiments demonstrate the efficacy of this approach in identifying damage in CFRP.
Delamination Damage Detection of CFRP Composite Structures Using DSAN-based Deep Transfer Learning Approach
Carbon fiber reinforced plastics (CFRP) is a kind of lightweight composite material widely used in aerospace. Since the progressive development of fatigue damage is complex and leads to potential safety risks in CFRP structures, structural health monitoring based on Lamb wave has been developed to track the growth of fatigue damages using a sensor network attached to the surface, which is experimentally intensive and expensive. To overcome the above challenges, a composite fatigue damage diagnosis method based on deep transfer learning is proposed to transfer the physical mechanism provided by numerical models to the diagnosis of real monitoring data. Firstly, numerical models of the composite structures are built to indicate the accumulation of fatigue damage during the full life cycle by introducing delamination between three sub-structures of cross-ply laminates. Then simulation signals with high fidelity are generated by virtual sensors and input into a data-driven diagnostic model with monitoring data. By aligning the data distribution of corresponding categories in simulation and experiment datasets respectively, the sub-domain adaptation is implemented and the physical mechanism provided by digital models is thereby fused with monitoring data. In this situation, the diagnostic model can still achieve more than 81% accuracy on a smaller training set, which performs better than conventional methods and significantly reduces the number of aging experiments.
Next Generation 3D-DIC Technique with Sensor-Based Extrinsic Parameter Calibration and Natural Pattern Tracking
Three-dimensional Digital Image Correlation (3D-DIC) is a computer vision technique capable of extracting full-field displacements of structures by processing images acquired from synchronized stereo cameras and performing triangulation. To properly reconstruct the 3D points in space, the stereo cameras must be calibrated to compute the lens distortion (i.e., intrinsic parameters) and the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking pictures of a calibration target (e.g., a checkerboard). However, the calibration target must occupy most of the cameras’ field of view (FOV) and have a size comparable to that of the object being tested, which becomes impractical for large-scale FOVs. Additionally, 3D-DIC requires the application of a stochastic speckle pattern on the surface to identify and track unique points across multiple images. This can be a problem when structures on which the application of a speckle pattern is impractical have to be analyzed (e.g., wind turbine blades). This research proposes a multi-sensor system using three inertial measurement units and a laser distance sensor to compute the extrinsic parameters of a stereo vision system and accelerate the calibration procedure. In addition, a feature-based natural pattern tracking algorithm is proposed to exploit features that are naturally present on the structure instead of applying a speckle pattern to perform 3D-DIC. Laboratory tests show that cameras calibrated using the multi-sensor system reconstruct displacements with an accuracy above 95% compared to displacements measured using a traditional calibration method. At the same time, the proposed feature-tracking algorithm can identify and reconstruct the 3D positions and displacements of preexisting features without requiring an applied speckle pattern. The research shows that the proposed methods can simplify and streamline the use of 3DDIC and open the way to the use of this technique to perform condition monitoring of large-scale civil and mechanical engineering structures.
A Robotic-Based Framework for Quantifying Surface Cracks of Concrete Shear Walls
The inherently subjective and manual nature of current structural assessment procedures undermines the reliability of such practices. As a result, researchers are using artificial intelligence and robots to encourage autonomy and create methods that are more objective and less biased. Robots are equipped with cameras and sensors to collect information about the structure, such as images of the crack patterns of concrete structures. In this paper, a robotic framework is proposed to utilize the collected images of reinforced concrete shear walls (RCSWs) with crack patterns, and then those images are used to quantify the level of damage. Crack pattern images are from the load steps of a quasi-static test on an RCSW. To quantify the extent of damage, crack patterns are first converted to a mathematical representation, a graph. Next, a machine learning algorithm is trained to predict the energy dissipated during each load cycle based on the graphs. Results reveal that the presented graph-based damage quantification method is able to quantify the level of structural damage. High R2 scores of the machine learning regression (above 0.98) attest to the success of the proposed robotic-based framework.
Task-Significance-Aware Meta Learning for Few-Image-Based Structural Damage Recognition
Recently, structural damage recognition has gained significant progress using deep learning and computer vision techniques. However, massive training images, the interclass balance and completeness of damage categories are essential to ensure recognition accuracy. In addition, the generalization ability for new damage categories and robustness under real-world scenarios are limited. This study proposes a task-aware meta-learning paradigm using limited images for universal structural damage segmentation. First, a novel task generation strategy instead of random sampling is designed based on feature density clustering. A synthetical metric of Jaccard distance and Euclidean distance is established to measure the feature similarity among multitype damage images. The class separability discovered in the high-level feature space of multi-type structural damage enhances the interpretability of randomly-generated tasks for conventional meta-learning. Second, a dual-stage optimization framework is built based on Model-Agnostic Meta-Learning (MAML), comprising an internal optimization stage of the semantic segmentation model (U-Net) and an external optimization stage of the meta-learning machine. Third, a set of core samples around the cluster center is selected to form an additional query pool and evaluate the tasksignificance scores of different tasks within a meta-batch by the same criteria. The task-significance scores are utilized in the external optimization to control the orientation of gradient updates towards more significant tasks. To verify the effectiveness and necessity of the proposed method, ablation experiments are performed using a multi-type structural damage dataset, including concrete crack, steel fatigue crack, and concrete spalling. The proposed method outperforms directly training the original U-Net and the conventional MAML algorithm using only a handful of training samples with improvements in segmentation accuracy. In addition, the improvement in recognition accuracy increases when using fewer training images, further indicating the efficacy of the proposed method. The generalization ability for new structural damage of steel corrosion is also demonstrated.
Hyperspectral Imaging Applied for Pixel-Level Crack Detection with Background Interferences
Cracks in civil infrastructures are an important sign of structural degradation and may indicate the inception of catastrophic failure. Existing image-based crack detection techniques face challenges when it comes to the complex background scenes. These irrelevant background interferences are common in practice and may trigger false alarms in crack detection. To eliminate their influence, hyperspectral imaging is employed in this study, which captures hundreds of spectral reflectance values in a pixel in the visible and near-infrared region. Compared with the conventional greyscale/RGB images which are limited to one/three wide spectral bands (red, green, blue), hyperspectral imaging can therefore provide more rich spectral information for crack detection/distinguish cracks from other background interferences. Due to the high correlations in hyperspectral image data, this study proposed a hyperspectral crack detection method using the low rank representation-based algorithm. Moreover, a locality constraint together with the dictionary learning process is incorporated into the proposed method to train a multi-class classifier. The built classification model is tested based on a real-world hyperspectral imaging dataset, which contains eight different surface objects in total. The trained classifier achieves an overall accuracy of 92.1%. The results show that the proposed method can predict cracks and other materials under complex scenes.
Automated Structural Surface Damage Identification, Classification and Severity Estimation Using Deep Learning Approaches
The maintenance of civil infrastructure has become a major challenge for modern engineers due to its continuous growth, especially buildings and bridges which are susceptible to structural damage caused by factors like aging, design flaws, and natural disasters, which can seriously jeopardize their safety, health, and structural soundness. The conventional inspection methods are not only costly and timeconsuming, but also pose safety hazards, rendering them ineffective. The paper introduces an automated method for detecting and assessing the severity of damages in buildings and bridges, which aims to address and mitigate the limitations of traditional techniques. A collection of 5000 images, ranging in size from 416x416 to 640x640 pixels, were gathered from damaged sites and annotated in polygon annotation format, with damages categorized into three classes: spalling, corrosion, and crack. To expand the dataset and enhance the precision of the deep learning models, data augmentation techniques from the data Albumentation library were utilized for image processing. Several object-based instance segmentation deeplearning models, including Yolo V5, V7, V8 Instance Segmentation, and Mask- RCNN, were trained on 80% of the dataset to obtain the coordinates of the damaged area's outline and generate masks for the detected damages. The area of these masks and the percentage of damage severity in an image were computed. The trained models achieved an accuracy range of approximately 60% to 70%, indicating the potential effectiveness of instance segmentation deep learning models. The proposed approach offers a quick and efficient method for determining the severity of structural damages, resulting in improved safety and decreased maintenance costs for buildings and bridges. To enhance the accuracy and precision of the model, future research could include a larger dataset with additional types of structural failures. Furthermore, the automated approach can be integrated into mobile monitoring and inspection devices, such as drones, as well as street camera systems to identify damaged buildings following natural disasters. An alarm can be triggered based on a specific threshold value, and appropriate actions can be taken to reduce the response time for emergency management and improve the structures' resilience to natural disasters.
Enhancing Dam Inspection with Pixel-Level CNN-FCN Approach Via 3D Texture Mapping
Inspecting damage through visual inspection is an outdated and inefficient method, especially for large concrete dam structures. Furthermore, inspecting for damage to dam structures is costly, time-consuming, and hazardous. This research proposes a novel method for detecting and measuring cracks in the concrete structure of dams by utilizing a combination of the Convolutional Neural Network (CNN)-Fully Convolutional Network (FCN) algorithm and image-based 3D modeling. The method enables the identification of cracks on large structures at a pixel level, facilitating detection over a wide area and with high accuracy. Specifically, the approach detects cracks on the threedimensional surface of the dam, providing a comprehensive and effective means of identifying potential structural issues. To accurately determine the size of the detected damage, the local thickness (LT) algorithm is introduced. This algorithm utilizes the pixelprecise detection capabilities of crack detection to provide highly precise crack size measurements. The CNN-FCN system segments cracks at the pixel level on the texture space obtained from a 3D model created through photogrammetry techniques. Then the trained CNN is employed to detect crack patches, which are then imported into a trained FCN system for pixel-level segmentation. The predicted crack pixels are converted to a physical scale by the LT algorithm to quantitatively measure the morphological features of cracks. The resulting crack map is then projected onto a 3D model, providing a comprehensive visual representation of the cracks. The proposed crack detection method can achieve an accuracy of over 90%. The study revealed that the presented system could efficiently and precisely detect, locate, and measure the size of cracks in large dam structures, highlighting the potential of the proposed method to enhance the efficiency and effectiveness of crack inspection. Furthermore, the visualization of cracks on 3D models can help increase inspectors’ inspection efficiency.
Real Time Condition Monitoring Using Camera and Phase-Based Motion Estimation
Defects on manufacturing equipment, such as abrasion, corrosion, and deposition, may decrease the production quality and even lead to the shutdown of the entire manufacturing line. Various condition monitoring technologies have been developed to predict machine health and safety through contact sensors. The noncontact camera has shown many advantages over the conventional contact sensors, including high spatial resolution, low cost and remote sensing. Moreover, phase-based motion amplification has proven to be an efficient tool for detecting subtle vibrations. However, the motion amplification requires significant computational resources and does not provide direct motion signal output. To detect abnormal vibrationsduring long-term inspections, a more efficient phase-based motion estimation technique is necessary. In this study, we propose a real-time vibration monitoring system that uses a camera and image phase. We use single optimal Gabor filter with phase-based optical flow to extract the vibrational motion. The use of single optimal filter significantly reduces computation costs and enables accurate measurement of vibration signals even in the low-light condition and/or with image noise. Parallel computing scheme is also introduced for real-time condition monitoring. We conducted validation experiments on a structure with multiple vibrating components that simulate a real factory line. Damage detection is performed on the structure with two damaged cases. All the results show that the proposed technique can accurately measure displacement and provide a novel solution for camera-based real-time damage detection.
Intuitive and Less-Supervised Structural Damage Detection Using Phase-Based Vibration Imaging
Numerous structural vibration monitoring techniques have been developed to identify structural operational conditions or structural damage. However, a high-level supervision from experts is usually required especially for applications on complicated structures. Cameras, as a noncontact sensor, have emerged as a powerful tool for measuring the full-field vibration of complex structures due to its advantages over the traditional contact sensors, such as flexible positioning, simultaneous multi-points tracking, and high-spatial resolution. However, extracting key information from the large amount of data from video sequences can be challenging. In this paper, we propose an improved phase-based optical flow method to efficiently estimate the full-field displacement, enabling visualization of the vibration energy distribution in a lesssupervised manner. The method consists of several steps. First, multiple Gabor filters are applied to extract the local phases at different scales and in different directions. The phase variations and spatial gradients are then used to estimate displacement components at each pixel. Sub-pixel level accuracy is achieved by rejecting unreliable components using confidence measures and then integrating them into the motion. The user only needs to provide inputs for two thresholds of confidence measures. Second, based on the extracted full-field displacements, the structural vibration is transformed into a series of images using feature extraction techniques, such as mean absolute deviation, which scales the amplitude of vibration level, and/or frequency filtering which visualizes operational deflection shapes in near real-time. Finally, a nonlinearity weighted local area mapping algorithm is proposed to detect anomalies present in a structure, which is done by calculating the difference in the full-field feature map between healthy and tested states. To validate our approach, we conducted on experiment on an air compressor and compared the results with those measured by accelerometers. The results showed that the proposed vibration imaging technique can clearly identify damage and indicate its potential for various structural dynamics applications.
Revolutionizing Road Maintenance: A Data Fusion and AI-Based Approach
The deterioration of pavement conditions over time can lead to significant costs for rehabilitation once the pavement conditions fall below a certain level of deterioration. To mitigate these costs, it is possible to extend the service life of pavement through periodic maintenance at a relatively low cost. To increase the frequency of inspections and enhance the effectiveness of maintenance activities, this study proposes a crowdsourcing-based inspection system for autonomous road condition assessment that can be mounted on multiple vehicles. The proposed inspection system can evaluate the pavement condition based on the Pavement Surface and Evaluation Rating (PASER) system. Vehicles equipped with this data acquisition system are driven over the road through the cities twice per week, thus comprehensive RGB-D data of the road surface are collected timely and widely. The system can detect various defects, including transverse cracks, longitudinal cracks, alligator cracking, and 3D defects such as rutting and potholes through a trained deep learning-based classification model. Furthermore, the proposed autonomous road inspection system can monitor the evolutionary changes of defects such as cracks and potholes to enable damage prognosis.
Enhancing Deep Learning-Based Damage Segmentation with Depth Hallucination
Recent studies have suggested that the fusion of cross-modal information can enhance the performance of deep learning-based segmentation algorithms. In this context, this study evaluates the benefits of RGB-D fusion with regard to damage segmentation in reinforced concrete buildings. The fusion of depth data was observed to enhance the segmentation performance significantly. Additionally, a number of surrogate techniques based on modality hallucination and monocular depth estimation are exploited to eliminate the need for depth sensing at test time without foregoing the benefits of depth fusion. The proposed techniques require depth data only for network training, and at test time, depth features are simulated from the corresponding RGB frames, obliterating the need for real depth perception. The proposed methods are evaluated and are shown to increase the damage segmentation accuracy.
Target-Free, Vision-Based System Identification of Civil Structures Using Unmanned Aerial Vehicles
Vibration-based structural health monitoring (SHM) frameworks rely upon accurately identified natural frequencies and mode shapes of structures in the field, which is critical information for damage diagnostics and model updating. Vibrationbased techniques have traditionally relied on discrete contact-based sensors. Despite the success of traditional sensing modalities, challenges and limitations remain: 1) the sensors need to be placed at discrete locations, and 2) the structure needs to be accessed for instrumentation. Advancements in the fields of computer vision and robotics have facilitated the use of remote sensing technology, such as cameras and Unmanned Aerial Vehicles (UAVs), in vibration-based SHM applications to address the limitations of traditional sensors. Although UAVs have been used to monitor the dynamic response of structures, these applications have primarily relied on targets or GPS to track the structure’s motion, which is not always feasible due to the large scale of civil structures. To this end, the main objective of this study is to develop an end-to-end target-free, vision-based framework for system identification of civil structures using UAVs. The proposed framework incorporates a phase-based motion estimation approach to extract the structural vibration information from videos without placing targets in the scene. For videos collected by UAVs, a correction needs to be applied to account for the camera’s rigid body motion. To compensate for this motion, the framework extracts the power spectral density (PSD) plot of a static object in the scene and subtracts it from the PSDs of the structure-of-interest. To evaluate the efficacy and robustness of the developed framework, an experimental study was conducted to monitor the free vibration response of two single-degree-of-freedom structures using three different UAVs in a controlled laboratory environment. The analysis shows strong agreement between the results extracted from UAVs equipped with high-resolution cameras with those from a stationary camera and those from accelerometers. Furthermore, the results show that camera resolution, alignment, and motion can significantly impact the accuracy of the results. This study shows the potential of successfully incorporating UAVs into target-free vision-based dynamic monitoring frameworks.
Accelerating Corrosion Surface-Area Measurements with Computer Vision and Deep Learning: An Ensemble Approach
The corrosion of infrastructure and facilities poses a significant challenge for the United States Department of Defense (DoD) in terms of cost and military readiness. To tackle this challenge, our research introduces a data-driven corrosion segmentation method that combines three deep learning-based models and an ensemble learning approach for the automatic identification and segmentation of corroded regions within high-resolution images. The method involves several stages, such as data annotation, preprocessing, augmentation, model implementation, and performance evaluation. The deep learning models used include Feature Pyramid Network (FPN) with Residual Network (ResNet)-34 encoder, UNet with ResNet-34 encoder, and UNet++ with Visual Geometry Group (VGG)-19 encoder. Ensemble learning, a technique that integrates these deep learning models, was employed to improve prediction accuracy and overall performance. The proposed method is evaluated using both the Dice score and the Intersection over Union (IoU) score metrics. Experimental results demonstrate that the ensemble learning approach outperforms individual models, achieving a Dice score of 90.1% and an IoU score of 83.9%. The approach shows promise to automatically detect and measure corrosion, which can reduce inspection costs and identify major issues to aid in prevention of structural failure. The tool developed in this study will be expanded to provide similar capabilities for large-scale civil infrastructure.
Unleashing the Power of Multi-Source Data for Building Attribute Prediction Based on Deep Learning in Flood Risk Assessment
Knowledge of a structure’s physical attributes is critical for analyzing the risks associated with natural disasters such as floods and hurricanes. However, collecting these data over a wide geographical area is a difficult task because it often requires manual labor. Even in Louisiana, which is at considerable risk for flood-related natural disasters, the state government has sparse structure level data about the composition of building type, number of stories, foundation type, foundation height, presence of a basement, and presence of a garage, all of which are significant predictors of flood risk in many flood risk assessment methods. In this study, an automated model is proposed to predict these six attributes by utilizing a data fusion-based methodology. The model extracts feature from a building’s Google Street View (GSV) image through a Convolutional Neural Network (CNN) architecture. Likewise, an Artificial Neural Network (ANN) architecture is employed to extract features from community data taken at the census-block-group level. Another ANN is used to extract features from structure-level data obtained through a real estate database. These three feature streams are then fused and processed with a fusion module to predict a building’s attributes needed for flood risk assessment. Through this technique, accuracies close to or surpassing 90% are achieved on all five classification tasks. Likewise, the Mean Absolute Error (MAE) for foundation height estimation is small enough to make usable improvements to flood risk estimates over existing data sources and modeling assumptions. Although CNN and ANN models are used in this study, the basic framework can be applied with other machine learning models as well, if features can be extracted from them. Likewise, all the experiments are performed in the context of flood risk assessment in Louisiana. However, the framework can be easily extended to other natural disasters and geographical regions.
Two-Dimensional Convolutional Neural Networks for Wood Quality Assessment
Different materials, including wood, have been tested using the contact Ultrasonic Testing (UT) technique. The time and velocity of the ultrasonic wave in a wood section have traditionally been monitored and correlated with wood quality. This practice, however, has not yielded satisfactory results, prompting researchers to develop new strategies to address the issue. In this study, the primary objective is to employ convolutional neural networks (CNN) to assess wood quality using the results of contact ultrasonic testing. To this end, 2D CNN models are employed to train on labeled ultrasonic signals as the training set. The developed models are thus set to solve supervised classification problems based on data gathered from testing specimens with various health conditions. The tested specimens are two types of wood with and without natural imperfections. Therefore, the size and shape of damage are different across specimens-billets harvested from trees at two sites in NSW and WA, Australia. This study aims to visualize and investigate the properties of the features extracted by the inner layers of the developed CNN models. This way, an unsupervised strategy can be devised to solve the clustering problem of woods based on their health condition.
Multiview Vision-based Displacement Measurement of Full-Scale Miter Gate
Miter gates are critical components of locks and dams of inland waterways for facilitating movement of vessels between different water elevations. Closure of locks and dams due to gate failure or unexpected maintenance stalls barge traffic, leading to goods hardly reaching the market as well as huge economic loss. Therefore, continuous condition monitoring of miter gates while not affecting their operation is especially important for the stakeholders. Measured displacements of the gates under load are known to be informative of their condition, as well as for the updating of numerical models of the structure. Considering the need for remaining operational, as well as inaccessibility of contact-type sensors, this study proposes a multiview vision-based displacement measurement strategy tailored for full-scale miter gates. A displacement measurement strategy using multiple cameras is developed specialized for the miter gate under harsh field conditions such as non-negligible camera motion induced by strong wind and lack of reliable reference points. The proposed method will be demonstrated on the full-scale miter gate at The Dalles Lock and Dam by collecting displacements of the gate during the water-fill of the lock chamber. The measured displacements can be compared to the corresponding numerical analysis outputs to inform the updating of the model, thus providing the potential to improve the condition monitoring of the miter gates on inland waterways.
Novel Approach for Imaging Time Series for the Improvement of Classification Results “Grayscale Fingerprint Features Field Imaging (G3FI)”
The remarkable successes achieved by Deep Learning (DL) in computer vision drew the attention of researchers to exploit this capability to apply it for the processing of time series and multivariate data. Thus, researcher began the development of suitable methods to transform time series signals into, images to enable the use of DL techniques to improve the classification of time series data. The most state-of-art techniques are Recurrence Plot (RP), Gramian Angular Field (GAF), or Markov Transition Field (MTF). These techniques transform each time series into RGB image. In this paper, a new transformation technique of time series and multivariate data to images technique is proposed. This technique is called <Grayscale Fingerprint Features Field Imaging" (G3FI). The main differences between this technique and state-of-art techniques are: a) the resulted image is a grayscale; b) the size of the resulted image is much smaller than the size of resulted images using state-of-art techniques. These differences provide the proposed technique with several advantages over the prior art, as it (a) results in avoiding redundant information and (b) noise, and (c) leads to a significant reduction in the required computational power. For the proof of concept, a dataset "Sonar, Mines vs. Rocks" is investigated and individually transformed using RP, GAF, and MTF, and the new developed technique "G3FI". The resulted images from each transformer are individually used to train and test Convolutional Neural Networks (CNN). The proposed method leads to competitive results in comparison to RP, GAF, and MTF.
Bridge Point Cloud Completion Using Deep Learning Obtained in Actual Bridge Structures
Point cloud, which can be obtained by optical measurement, is recently recognized to be useful in SHM for the maintenance and management of existing civil structures. However, there are some issues in measuring point cloud of large structures such as bridges. First, multiple measurements from different locations are required to reconstruct the point cloud of a whole structure. Second, the parts, where are interrupted by trees or other objects cannot be measured well even in uses of cameras or 3D scanners. Therefore, the point clouds acquired in actual structures cannot prevent missing parts or lack of details of structural configurations. This study aims to show applicability of deep learning for reproducing the partial point cloud obtained from measurements in actual structures into a completed point cloud. The experiment results show that even with limited data, transfer of training weight and component-wise completion can yield greater accuracy compared to completion of the entire bridge.
Photorealistic Rendering and Modification of Motion Blur for the Simulation of Video Data for Structural Dynamics
In recent years imager-based approaches to structural dynamics measurements have gained increasing interest. Imager-based approaches to measurement have a number of attractive properties including being able to monitor large areas at a relatively high spatial resolution with a relatively small number of imagers. The stand-off monitoring capabilities of imagers are also highly attractive for many structural dynamics and structural health monitoring applications, particularly those in dangerous, inaccessible, extreme, and high temperature environments. For their advantages though, a number of challenges must be addressed when using imager-based techniques for measuring the deformation and motion of structures. There are issues of lighting conditions that can potentially vary during the measurement period, due to movement of the sun or lights in a facility being turned on and off. In addition, video measurement of dynamic structures also requires making a number of choices associated with the measurement setup including location of the imagers, focal lengths of lenses, numberof- pixels, lens characteristics, framerates, shutter speeds, ISO, aperture settings, planes of focus, and depth-of-field. There can potentially be effects such as specular reflections or shadows which might complicate downstream data processing. The large number of parameters associated with imager measurements make them very flexible, but it also can mean that setting up imager measurements can take substantial time even under controlled laboratory conditions. The complication associated with imager measurements suggests that highfidelity, photorealistic rendering tools that are capable of capturing the interactions between structural dynamics, light transport, and the measurement process at the imaging plane are needed. To date, substantial work has been done by the computer graphics community to develop photorealistic rendering tools which are becoming increasingly accessible. However, in structural dynamics we often care about sub-pixel motion and it is not understood whether current techniques for modeling effects such as motion blur have sufficient fidelity for structural dynamics. Furthermore, photorealistic video renders can take a large amount of time to complete, and given current techniques do not allow for simple changes such as frame-rate/shutter speed in post processing without redoing the entire render. In this work we present an approach based on digital coded exposures for forming frames of dynamic scenes, that allows for the framerate and shutter speed to be changed in post-processing. The proposed approach is inspired by the physical operation of conventional cameras. In addition, the digital coded exposure proposed approach suggests theoretical alternatives to data capture that could potentially be used to control motion blur properties on a per-pixel basis.
High-Resolution Vision Transformers for Pixel- Level Identification of Structural Components and Damage
Visual inspection is predominantly used to evaluate the state of civil structures, but recent developments in unmanned aerial vehicles (UAVs) and artificial intelligence have increased the speed, safety, and reliability of the inspection process. In this study, we develop a semantic segmentation network based on vision transformers and Laplacian pyramids scaling networks for efficiently parsing high-resolution visual inspection images. The massive amounts of collected high-resolution images during inspections can slow down the investigation efforts. And while there have been extensive studies dedicated to the use of deep learning models for damage segmentation, processing high-resolution visual data can pose major computational difficulties. Traditionally, images are either uniformly downsampled or partitioned to cope with computational demands. However, the input is at risk of losing local fine details, such as thin cracks, or global contextual information. Inspired by super-resolution architectures, our vision transformer model learns to resize high-resolution images and masks to retain both the valuable local features and the global semantics without sacrificing computational efficiency. The proposed framework has been evaluated through comprehensive experiments on a dataset of bridge inspection report images using multiple metrics for pixel-wise materials detection.
Structural Nonlinearity Extraction from Video Data for Damage Evaluation in Earthquake Events: Experimental Verification
A development of a method of structural nonlinearity extraction is introduced for fast evaluation of structural damage conditions in post-earthquake events using the video data that is taken and shared in societies. The video data-based structural analysis has advanced rapidly in recent years due to advantages of non-contact data acquisitions, high spatial resolution in low-cost device, and so on. This study presents the experimental verification conducted by the shaking table tests, and to extract singularities spatial domains due to nonlinearity events using computer vision (CV)- based technology. In the shaking table test, two three-story aluminum frame models, whose modal frequencies are designed to become slightly different, are used to introduce some kinds of structural nonlinearities, such as hitting, boundary condition changes, and residual deformations in some of members. The present study solely concentrates on boundary condition nonlinearity, which is achieved by incorporating a controllable hinge member with a trigger magnet at the base of the columns to simulate the boundary constraint change from fixed end to hinge end during excitation. When the input excitation level is large enough, the magnets detach and cause the boundary constraint to change. A video camera is put in front of the model to capture the whole experiment, and the acquired video data are used for the verification. Optical flow method, as an effective video processing technique, can be used to estimate the real-world object motion between observer and scene by a dense field corresponding to the interframe displacement of each pixel. Farneback optical flow algorithm is selected here to extract the motion information of models; Then, the node strength index is introduced as the feature extraction to detect the nonlinearity events. The change in boundary conditions can significantly alter structural local motion information, leading to a clear mutation in node strength that can serve as an indicator of nonlinearity events. The results in this experimental study show the possibilities of video-based technology for the fast damage condition evaluation in the post-earthquake.
Integrated Technology for Detection and Repair of Mechanical Component Surface Fatigue Microcracks Using Pulsed Laser
During processing and service, various types of fatigue microcracks inevitably occur on the surfaces of mechanical components. If the microcrack is not repaired in a timely manner, it will continue to expand, affecting the safety and stability of the equipment. To this end, we introduce pulsed laser technology and propose an integrated technology for the detection and repair of mechanical component surface fatigue microcracks using pulsed laser. A finite element model is established to simulate the nonlinear interaction between fatigue microcracks and laser-generate Rayleigh wave, and the effects of parameters such as crack depth and width on the acoustic nonlinear index are investigated. On the basis of detection, the feasibility of the fatigue microcrack repair using laser melting effect is investigated. Simulation results show that contact and friction between crack interfaces must be considered, and the nonlinear modulation for Rayleigh wave mainly originates from the nonlinear interaction between the longitudinal wave component and the fatigue microcrack. The acoustic nonlinear index of longitudinal wave increases and decreases with the increase of crack depth and width, respectively. In addition, fatigue crack repair is feasible by properly adjusting laser parameters.
Development of an Artificial Intelligence (AI) for the Prediction of Fatigue Failure in Naval Structures: A Digital Twin Application
Many of the high speed and high-performance naval vessels use aluminum as a primary structural material. Welded aluminum stiffened structures are very common in these vehicles, but even with much research, performance of these structures under fatigue is not completely known. Many experimental investigations on similar structures were performed over the past two decades. Meanwhile, the desire for a comprehensive naval structural health monitoring (SHM) system is receiving greater interest. Recently, with re-advent of machine learning and artificial intelligence (AI) for structural digital twin, older experimental sensor dataset from crack growth in welded aluminum structure could be better utilized and exploited for crack predictions with few interventions from the sensor data. In this study, piezoelectric wafer active sensors are utilized for SHM of a welded aluminum structure and sensor data were collected at multiple frequencies from multiple specimens. Crack initiation to growth pattern were also recorded. Through machine learning, sensor data from four specimens were exploited to develop an AI algorithm for predicting the crack growth. It is shown that ML and AI frameworks are suitable for ship structure digital twin applications.
Damage Localization Frameworks Based on Unsupervised Deep Learning Neural Networks
In recent years ultrasonic-guided waves (UGWs) have been successfully employed in structural health monitoring (SHM) for damage localization due to their high sensitivity to changes in the mechanical properties of the medium they travel through. Lamb waves (LW) are a particular type of UGW that can be generated by piezoelectric transducers placed on thin-walled structures, such as vehicles in general (terrestrial, naval, and aeronautical), and present characteristics that are favorable to SHM. Damage localization using LWs has been commonly accomplished through tomographic algorithms. However, these methods have unresolved issues such as artifacts generation in damage probability maps and a strong reliance on sensor network configuration for signal acquisition. As a solution, data-driven approaches based on supervised machine learning have been suggested. These methods have demonstrated good performance. However, for reliable results, they require large, labeled datasets, meaning that acquisitions must be performed before and after the structure is damaged. These datasets, especially data from the damaged state, are generally not available for real-life structures, given the cost and complexity to experimentally replicate certain damages. Unsupervised machine learning methods might be a solution to this problem, given that the neural network is trained using data acquired from the un-damaged structure only. To this date, no fully unsupervised damage localization frameworks have been proposed. Hence, in this work, two unsupervised data-driven methods are presented to process LWs to localize damage. Specifically, convolutional auto-associative neural networks (CAANNs) and generative adversarial networks (GANs). Both methods process diagnostic signals without requiring any prior feature extraction. After all signals are processed, a damage probability map is generated. The performance of both methods is tested using an experimental dataset of LW acquisitions using a set of piezoelectric transducers on a full-scale composite wing. Results showed that the proposed methods have good damage localization accuracy.
Deep Learning-Based Automatic Blind Identification Procedure for Structural Modal Identification
Traditional modal identification approaches require too much user interaction, which results in low implementation efficiency. This study develops an automatic approach that combines deep learning and blind source separation to determine the modal parameters of structures. The core object of the proposed approach is to establish a multi-task deep neural network (MTDNN) that enables it to automatically obtain independent modes from multi-mode vibration responses of structures. Then modal frequencies and damping ratios of structures can be extracted from independent modes by employing the traditional random decrement technique and curve fitting approach. The weights between the last two layers of MTDNN represent the corresponding mode shapes. The approach is implemented in a long-span cable-stayed bridge in engineering practice for validation. For the field test, five accelerometers are employed to derive acceleration responses of the structure. The results indicate the ability of the proposed approach to automatically determine the modal parameters of structures with reliable accuracy, which provides a promising new solution for online modal parameter identification and modal tracking of structures.
Structural Parameter Identification with a Physics-Informed Neural Networks-Based Framework
Structural parameter identification is a critical aspect of structural health monitoring and maintenance. It belongs to inverse problems, which aim to discover mechanical parameters, such as the stiffness of the concerned system from collected measurement data. Then, the identified parameters are used to evaluate the status of the structure and detect potential structural damage in advance. As a promising machine learning method that effectively combines domain knowledge and deep neural networks, physicsinformed neural networks (PINNs) have been widely applied in various fields, including structural parameter identification and structural health monitoring. In this study, we propose a novel framework based on PINNs for parameter identification in structural systems. The framework contains two main components, one is called the physical term that employs PINNs to learn the prior physical knowledge of the structural system; the other is called the discrepancy term that involves another PINNs or even a simple feedforward neural network to present the differences between the observed data and the physical term. In a nonlinear structural system, the physical term can be regarded as the linear part of the structural response, while the discrepancy term represents the nonlinear response. In the PINNs configuration, the residuals of the governing equation, which are calculated by substituting collocation points that are randomly sampled within the domain into the governing equation, are directly incorporated into the total loss function. In addition, the boundary conditions, as well as the initial conditions, are softembedded as essential parts in the total loss function. The measurement data is also required by an inverse problem, and the differences between the measurement data and the prediction produced by the PINNs-based physical term will be captured by the discrepancy term. Through the discrepancy term, the nonlinear structural parameter can be figured out. Finally, a two-degree of freedom mass-spring system with nonlinear spring is investigated to illustrate the ability of the proposed framework in structural parameter identification.
GTRF: A General Tuples Recognition Framework Towards Deep Learning-Driven Structural Health Monitoring Adapted to Diverse Supervision Paradigms
Leveraging the powerful capabilities of deep learning (DL) techniques, the DL-driven tuple recognition approach has successfully addressed numerous challenges in SHM by associating tuples with structural patterns. However, the robustness and generalizability of the model are significantly compromised due to limitations in their designated feature extraction strategies, network architectures, and supervision learning schemas. To address these issues, this study proposes a novel General Tuple Recognition Framework (GTRF) that supports supervised (SL), unsupervised (UL), and semi-supervised learning (SSL) paradigms. The present article provides a detailed explanation of the mechanism and workflow of the proposed GTRF. Equipped with sophisticated networks and innovative components, the GTRF demonstrates competence in various tuple recognition tasks across different learning paradigms. The validation experiments conducted in the field of SHM include vibration SL-recognition of a prototype skyscraper, damage UL-detection of a laboratory RC beam, and condition SSLassessment of a full-scale building model. To ensure the adaptability of diverse tuples, two commonly used data forms, namely acceleration measurements and piezoelectric signals, are employed in the experimental validations. The comprehensive results confirm the effectiveness and adaptability of the proposed GTRF. The flexible paradigm specialization, broad application, and potential for optimization make the proposed GTRF framework a promising prototype for bridging the gap between DL algorithm fusion and model integration across different learning paradigms.
Exploring the Potential of Transfer Learning Applications for Structural Damage Classification
Structural Health Monitoring (SHM) is essential for ensuring the safety and maintaining the functionality of structures and infrastructure systems, and Machine Learning (ML) techniques have shown great potential in rendering SHM as an automated process. However, unlike many other application areas of ML, when dealing with infrastructure SHM, there is little to no data available from damaged states to be used for ML training. To cope with this issue, a typical Finite Element Model (FEM) of a representative bridge beam structure is used for generating a damaged dataset. Secondly, creating a generalized network that can successfully work and perform a classification task for a variable set of structures seems to be one of the biggest challenges in the field of structural damage detection. In this work, the capability of Transfer Learning (TL) via Feature Extraction (FE) and Joint Training (JT) in generalizing the network is explored. Modeling uncertainties for supervised damage classification can be mitigated by utilizing a Deep Neural Network (DNN) comprised of Fully Connected (FC) and Convolutional (CONV) layers. In this regard, FEMs of three simply-supported beam structures with varying lengths were constructed, and acceleration time-history data was obtained from the interior nodes under the applied load history at the midpoint. The damage states were represented by the change in flexural stiffness within a range from 10% to 90%. Subsequently, the potential knowledge transfer was successfully implemented from the source domain to the target domains via TL and JT. Then, the proposed two-dimensional (2D) CNN network was tested with a target dataset that was not included in the training from another beam with a different length. The results indicate that FEMs could generate numerous source domains to achieve a generalized model even with including uncertainties.
iFlyNet: Inferring UAV Flight from Wing Behavior
We introduce iFlyNet, a novel flight state awareness system that is robust, lowprofile, and more reliable in harsh conditions than conventional methods. The system interprets the global flight state of a UAV by monitoring multimodal structural characteristics of its wings, where aerodynamics are predominant. A microfabricated transducer array consisting of temperature compensated strain and piezoelectric sensors captures the wing’s static and dynamic stress profiles at multiple spatial locations. This data informs our deep one-dimensional convolutional neural network-based sensor fusion algorithm designed to run on flight vehicle computer at near real-time speeds. Wind tunnel experiments with the sensors installed on a single wing demonstrate that iFlyNet can accurately predict the flight state variables of airspeed and angle of attack across the flight envelope of the UAV. In addition, we also show the benefits of our system compared to status-quo techniques through accurate predictions of lift and drag forces and high-precision tracking of stall occurrence at highly variable environmental conditions. By nearly matching the readings of traditional state measurement devices at a fraction of weight as well as providing accurate predictions of flight performance and safety-critical metrics, our system offers a unique paradigm for aircraft state identification.
Population-Based SHM Under Environmental Variability Using a Classifier for Unsupervised Damage Detection
In this paper, we introduce a novel deep learning technique for anomaly detection in the context of Population-Based Structural Health Monitoring (PB-SHM). The proposed method eliminates manual feature engineering by utilizing Power Spectral Density (PSD) as input, allowing examination of the entire spectrum. It is based on an auxiliary classification task, wherein the model is trained to discriminate between different systems according to their dynamic response. The classifier confidence is then used during inference for damage detection. The neural network extracts discriminative features commonly impacted by damage, which are employed to create a normality model. The efficacy of our method is demonstrated on a simulated population of 20 individual 8- DOF systems influenced by a latent environmental variables, emphasizing its potential for PB-SHM under diverse conditions. Our technique achieves performance comparable to resonance frequency-based methods while potentially exhibiting higher capability in complex structures with multiple modes. Anomalies caused by a 5% decrease in stiffness are successfully detected, yielding an AUC of 0.94.
Fundamental Study on Damage Detection of Civil Structures Modeled as MDOF System Based on Machine Learning
In Japan, the deterioration of civil structures constructed during the period of strong economic growth combined with the shortage of engineers has become a serious social problem. One solution to address this issue is to improve the efficiency of structural maintenance and management using sensors. The application of machine learning method is expected to advance such efforts. This research attempts the damage detection of civil structures using an autoencoder, which is a type of unsupervised machine learning. The study revealed that there is a relation between the magnitude of reconstruction error and the degree of damage to the structure.
Enhancing the Reliability of Structural Health Monitoring for Bolted Joint Connections in Segmented Rotor Blades Using Data Fusion
In the wind energy sector, the use of segmented rotor blades poses challenges for Structural Health Monitoring (SHM) systems due to the increasing high loads that can damage bolted joints. This paper investigates the potential of data fusion to improve the reliability of SHM systems for bolt connections in segmented rotor blades. Three CFRP structures with bolted joints are examined and monitored using both piezoelectric and electrical strain gauge sensors. A passive low-frequency monitoring system is built using strain measurements and neural networks to model the relations between measurement data. An additional active high-frequency monitoring system is designed using piezoelectric sensors and guided waves in combination with a subsequent principal components analysis. The results show that the data fusion system successfully detected the damages with an accurate damage occurrence probability prediction even if one monitoring system provided unreliable predictions. By combining two independent monitoring systems that complementarily cover different frequency bands, the data fusion system improved the reliability of the monitoring task and reduced the occurrence of false positive alarms. The approach presented in this study can also be applied to other monitoring systems in various industries, further expanding the impact of the research.
Unsupervised Damage Detection for Smart Extraterrestrial Habitats Using Autoencoders and Information Fusion
In the context of smart and resilient extraterrestrial habitats, the structural health monitoring (SHM) of habitats is crucial and challenging due to the harsh space environments. To this end, a novel anomaly detection framework is developed based on active sensing and information theory. Active sensing involves the excitation of the structure at specific locations and collecting acceleration data using sensors. The collected data from different excitation points and sensor locations are analyzed, and the extracted information is fused to further enhance anomaly detection. More specifically, an unsupervised anomaly detection framework using autoencoders (AEs) has been developed. Continuous wavelet transforms (CWTs) of acceleration signals are utilized to train AEs. Information fusion strategies are proposed to enhance the robustness of the approach to both aleatoric and epistemic uncertainties. Two unsupervised learning approaches developed by standard AE and variational autoencoder (VAE) are systematically compared. The numerical study based on the ASCE benchmark model and the experimental study based on a geodesic dome testbed have been carried out to validate the performance of the proposed framework and study its limitations. The framework's ability to extract information from multiple sources allows it to identify anomalies that might have been missed by traditional detection methods. For instance, it is shown that the proposed approach increases anomaly detection accuracy by up to 39.8% under a relatively small damage scenario compared to state-of-the-art approaches.
When is an SHM Problem a Multi-Task- Learning Problem?
Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for the context of structural health monitoring (SHM): (i) the natural occurrence of multiple tasks; (ii) using outputs as inputs (both linked to the recent research in population-based SHM (PBSHM)); and, (iii) additional loss functions to provide different insights. Each of these problem settings for MTL is detailed and an example is given.
Low-Energy Impact Characterization Models and Damage Detection in Thin Monocoque Structures Using Artificial Intelligence
The low damage tolerance of aircraft structures leads to an oversizing of their optimum weight as they are designed to withstand impacts on aerodynamic surfaces. Through the application of Structural Health Monitoring (SHM) techniques, which allow the characterization of impacts with a reduced number of sensors, further structural optimization is possible. This article proposes the use of Artificial Intelligence (AI) models for a complete impact characterization performed on an anisotropic Carbon Fibre Reinforced Plastic (CFRP) plate. The three principal objectives are impact location, characterization of the Impactor Object (IO), focusing on splitting the mass and velocity at equienergy impacts, and damage detection. The location task consists of the prediction of the impact coordinates (X-Y), while the energetic characterization of the IO predicts its mass, velocity, and energy when the impact occurs. These models are powered by low-cost piezoelectric (PZT) sensors, which acquire the acoustic wave generated by the impact, which allows the monitoring of large surfaces of complex geometry with a reduced number of sensors. These models have been trained with experimental data acquired with an Autonomous Impact Machine. This machine has performed more than 40,000 impacts on a coordinate grid that vary the IO mass, velocity, and impact energy, focusing on multiple equal energy combinations of the IO mass and impact velocity. Damage detection is performed by comparing the predictions of multiple location and characterization models.
Surface Damage Identification in 3D Printed Metal Parts Using Convolutional Neural Network
Surface Response to Excitation (SuRE) is an active Structural Health Monitoring (SHM) method used in this study for the detection and quantification of the artificial damages created by the milling operation on additively manufactured metal plates. In this method, one piezoelectric element is bonded to one end of the test specimen to excite it with surface waves and the dynamic response to excitation is recorded by another piezoelectric at the other end of the part. The excitation signal is a sweep sine wave with a duration of 1 ms and a frequency range of 50-120 kHz. Using a Markforged metal 3D printer, five stainless steel plates of identical size (195×54×2.5 mm) were created. The data was recorded when all the parts were in a healthy condition and when they were face milled at 3 different lengths. The collected sensory data in the time domain were converted to time-frequency representation images using continuous wavelet transform (CWT). Different data augmentation methods have been implemented for expanding the size of the dataset. The image dataset was used as input to train a Two-Dimensional Convolutional Neural Network (2D-CNN) for the detection of damage and also for quantifying the damage length. The CNN could detect the damaged parts with 97.4% accuracy and classify the damage length with an overall accuracy of 98.7%.
Enhancing Structural Health Monitoring and Management Through Edge, Fog and Cloud Computing Architectures
The aim of Structural Health Monitoring (SHM) is to detect, locate, and quantify structural damage through data acquisition, which not only enhances human safety and minimizes infrastructure maintenance costs but can also be performed in real-time. However, environmental factors and operational risks can limit the effectiveness of SHM techniques. Fortunately, the emergence of smart connected devices and internet connectivity has enabled remote monitoring of various structures such as buildings and bridges from any location and at any time. Despite numerous advancements in Internet of Things (IoT) technology in Structural Health Monitoring (SHM), it still struggles with latency issues when analyzing and visualizing real-time data from various structures. Current cloud-based architectures are not efficient in displaying various structure metrics, such as acceleration, humidity, and temperature. Additionally, utilizing state-of-the-art machine learning techniques for predictions is time-consuming as data must travel long distances to reach the cloud. To address these challenges, emerging technologies like edge computing and fog computing can be implemented. Edge computing aims to bring processing and storage as close as possible to the application, while fog computing complements cloud computing by handling fewer intensive analytics and processing tasks. The proposed work showcases a multilayer system architecture comprising edge, fog, and cloud computing, designed to collect, analyze, and visualize sensor data via Amazon Web Services (AWS). This approach involves connecting multiple sensors to an edge device (Raspberry Pi) to develop a standalone web monitoring interface. Furthermore, machine learning algorithms are deployed on the edge to predict the local behavior of a structure using its local data. The methodology of monitoring structures using the architecture presented in this paper exhibits great potential. The effectiveness of the current approach is demonstrated through a comparative analysis of its performance and latency. Besides the software could potentially be packaged and offered as Software as a Service (SaaS) product in the future.
Impact Force Identification on Steel Pipelines Using Deep Learning and Bayesian Inference with Minimal Sensing Elements
Pipelines are crucial for various industries but are vulnerable to impact damage, making it essential to identify impact forces for maintaining structural integrity and preventing accidents. Traditional inverse methods for impact force identification can be expensive, time-consuming, and require pipeline shutdowns. To address these challenges, this study proposes a novel approach using deep learning and Bayesian inference techniques with minimal sensing. Four accelerometers were placed on a 6- meter-long steel pipeline, and data was collected, pre-processed, and split into training and testing sets. A 5-layered ANN model was trained, achieving 87.2% accuracy, which was then used as a surrogate model for the Bayesian inference. The proposed approach, based on Approximate Bayesian Computing with Subspace Simulation (ABC-SS), demonstrated a reliable and robust solution for identifying impact forces on pipelines.
On the Influence of Structural Attributes for Assessing Similarity in Population-Based Structural Health Monitoring
The viability of many machine learning methods within Structural Health Monitoring (SHM) is often limited by the lack, or the incompleteness, of the data required for implementing these algorithms. Indeed, learning a data-based SHM predictive model usually requires the dynamic response availability for undamaged and damaged states, and the assumption that both training and test data refer to the same domain. In this framework, the population-based approach to Structural Health Monitoring (PBSHM) aims at improving the performance and the robustness of diagnostic inferences, exploiting the transfer of damage-state knowledge across a population of structures. However, sharing these data produces a meaningful inference only if the structures, and their datasets, are sufficiently similar. Therefore, an initial phase of similarity assessment becomes essential before being able to apply transfer learning algorithms. This phase shows which structures are suitable for knowledge sharing, if any, reducing the possibility of negative transfer. Some distance metrics have been proposed, exploiting abstract representations of structures, such as Irreducible Element (IE) models and Attributed Graphs (AGs). Although these metrics can consider the structure attributes, many performed comparisons mainly concern structural topology. This study aims at broadening the application of similarity assessment, focussing on the geometrical and material differences in the distance metrics. Therefore, a heterogeneous population of laboratory-scale aircraft is analysed. These structures predominantly follow the geometry of a benchmark study conducted by the Structures and Materials Action Group (SM-AG19) of the Group for Aeronautical Research Technology in EURope (GARTEUR). The IE models of these aircraft are produced. Subsequently, Graph Matching Network (GMNs) are used to determine the similarity matrix. The structures in the Garteur population are topologically homogeneous, which enables a more accurate investigation of how attributes can influence distance metrics. This paper constitutes the first step in the Garteur structures population investigation. The similarity assessment results will establish which population members are most suitable for applying transfer learning algorithms. It will enable the subsequent development, and experimental validation, of a population-based strategy for damage identification across heterogeneous structures.
Graph Convolutional Neural Networks Based Strain Estimation
Infrastructural owners have to manage and control a huge number of structures that are subject to many phenomena that can alter their performance. During their service life, infrastructure like bridges are exposed to cycles of dynamic loads, causing potential fatigue failure. For the life-cycle assessment aim, most procedures require the use of strain data. The acquisition of strain data is not easy, especially in critical positions, and the high costs would restrict the wide use of such procedures. This study proposes a technique based on the graph theory to obtain strain from acceleration data. As shown in neuroscience and biological fields, this type of architecture is able to achieve good outcomes not relying on handcrafted design parameters. Besides, their capacity of grasping spatio-temporal dependencies turns out to be a key point in the damage detection framework. A numerical case study involving a two-span beam has been investigated to explore the potentiality of this architecture for acceleration-strain conversion. Here the graph nodes represent the sensor locations and the combination of geometric and signals aided to create a spatial-temporal GCN architecture. The obtained outcomes are promising and make the framework extendable to real case studies.
A Comparative Analysis of Concept Drift Detection Methods with a Systematic and Innovative Approach of Method Selection
One of the most significant challenges in data-driven modeling of complex systems is dealing with concept drift, i.e., the unpredictable changes in the underlying data distribution over time. In this work, nine concept drift detection (CDD) methods are evaluated with respect to different types of concept drift, including abrupt, gradual, incremental, and real concept drift, for both supervised and unsupervised application scenarios. For the supervised case, the methods EDDM, FHDDMSadd, MDDM-E, and EFDT are compared against a classification without change detection using Naïve Bayes as the base classifier. In the unsupervised application scenario, CluStream, ClusTree, DenStream, StreamKM++, and D-Stream are evaluated. The experiments are conducted using the Massive Online Analysis (MOA) evaluation platform, and the performance of each method is measured in terms of classification accuracy, memory consumption, and computation time. This empirical research shows that classification accuracy can be improved by 20% by implementing a CDD method, highlighting the importance of CDD in SHM data streams. However, there is no single method that proves to be superior in all scenarios, and the choice depends on the characteristics of the considered data stream and application requirements. Selecting the appropriate CDD method from the approximately 340 different methods found in the literature is not a trivial task and can lead to suboptimal selection. To tackle this issue, an innovative approach is proposed to assist researchers and practitioners find the appropriate CCD method for their application.
Implementation of Information Entropy in an Industrial Internet of Things Approach for Structural Health Monitoring Applications
Structural Health Monitoring (SHM) involves damage assessment processes that contribute towards overall safety decisions. The need for real-time assessment and decision-making in SHM has long been attempted in various ways via connections between data acquisition and information extraction. In this context, this investigation presents a novel approach to enable real time data streams for SHM. To achieve this goal, an Industrial Internet of Things (IIoT) framework developed is used in conjunction with Nondestructive Evaluation (NDE) datasets for near real-time diagnostics. To demonstrate the performance and results of applying this method, the case of laboratory scale testing of crack initiation is presented in this manuscript. Specifically, compacttension specimens of an aerospace-grade aluminum alloy were used in accordance with ASTM standards. Acoustic Emission (AE) datasets were acquired and were subsequently used in an in-house built, scalable IIoT system capable of edge, fog, and cloud computing. At the fog layer, a trained model was loaded to classify the signals in real-time. The trained model relies on signal Information Entropy (IE) values as input and outputs to form an indicator of crack initiation. The AE data input is shown as a test-case for any general time-series type data acquired in SHM applications such as accelerometers and vibration sensors. The main innovation of this approach is the fact that a combination of hardware, computing and IE analysis proves to be advantageous to flag the incubation and subsequent initiation of fracture. The IIoT system described can be applied to a variety of SHM applications for continuous type monitoring.
Bidirectional Long Short-term Memory Network for Maglev Bridge Acceleration Data Reconstruction
Maglev is a developing transportation mode in the recent years, and its safety and stability are required to be ensured by structural health monitoring system. However, the data measured by some sensors may be partially or totally unavailable due to the external electromagnetic interference or sensor failure. While, in some cases, those missing data are important to identify and analyze damage of maglev bridges. Since the structural dynamic characteristics of maglev bridge are complicated, the data reconstruction of those sensors becomes challenging. Therefore, this study proposes a bidirectional long short-term memory (BiLSTM) network to accurately reconstruct the maglev bridge acceleration data. The design of long short-term memory helps to sufficiently exploit the time series data and learn the hidden features of maglev bridge acceleration data between sensors. The bidirectional architecture enables the network to simultaneously learn the time series data from past and future, which is beneficial to extract more hidden features. A dataset collected from an in-site experiment for maglev bridge is used to verify the feasibility of the proposed method. The lost acceleration data from abnormal sensors is predicted by the acceleration data recorded from normally operating sensors. The results shows that the difference between predicted and true acceleration data is at a very low magnitude. Consequently, the proposed method can be applied for the high-performance reconstruction of maglev bridge acceleration data.
Vehicle-invariant Drive-by Monitoring Across Multiple Bridges through Bootstrappingenhanced Unsupervised Domain Adaptation
Bridge health monitoring (BHM) is important to detect damages in the early stages to avoid loss of human life and any disruption in the continuous bridge operations. Driveby vehicle-based BHM approaches provide more scalable monitoring as compared to manual inspection and fixed sensors on bridges. Each vehicle can pass multiple bridges and can be used for monitoring multiple bridges. In our prior work, we developed a method that can diagnose damage in multiple bridges while eliminating the burden of collecting labeled data from all the bridges. It is achieved by learning features that are sensitive to damage and invariant across bridges. However, in real-world scenarios, vehicles passing the bridge possess varying properties such as suspension system, driving speed, and vehicle mass. Since the vibration signal obtained from the vehicle depends on the vehicle’s properties, these variabilities in vehicle properties lead to inaccurate damage prediction of the bridge even for the same damage state. To overcome these challenges, we introduce a Bootstrapping-enhanced Vehicle - Bridge-Invariant (BeVBI) approach for robust drive-by BHM. It reduces the vibration signal variation due to varying vehicle properties through bootstrapping-based mean estimations. Specifically, vibration signals obtained from vehicles (with varying vehicle properties) passing the bridge are randomly aggregated with replacement (i.e., bootstrapping) and averaged. Based on the central limit theorem, averaging the aggregated signals (bootstrapped signals) reduces signal variability due to vehicle properties by the square root of the number of aggregated signals. Further, these bootstrapped signals are used to predict the damage on multiple bridges by adopting an unsupervised domain learning algorithm. The performance of the above approach is evaluated using a numerical vehicle bridge interaction dataset with two different bridges and 4800 drive-by vehicles having different dynamic properties and speeds. Our approach is successful in diagnosing multiple bridges while being robust to varying vehicle properties. It performs 1.45x better in the detection and localization of damage and 1.75x better in the quantification of damage as compared to baseline methods (MCNN and HierMUD).
Optimization of Damage Features Contaminated by Nonstationary Colored Noise Algorithms using Johansen Cointegration
For structural health monitoring (SHM), researchers have primarily investigated the effects of signals contaminated with stationary white noise, with very few studies examining pollution caused by highly correlated nonstationary colored noise, such as Brown noise. To address this issue, this paper proposes an optimization-based damage detection technique for composite structures exposed to nonstationary colored noise using condensed frequency response functions (CFRF) as damage-sensitive features (DSF). Two different signal-to-noise ratios (SNR) of Brownian motions, i.e., 20 and 10, are used in the investigation to contaminate CFRFs. Contamination produces nonstationary patterns, making it difficult to detect damage with vibration-driven methods. In this study, we propose a new goal function based on Johansen cointegration, an econometric concept. The proposed objective function converts nonstationary CFRF signals into stationary representations, subsequently fed into optimization-based model updating algorithms. The Reptile Search Algorithm (RSA) is employed to update unknown structural damage indices based on the constructed objective function. The new method is validated on a finite element (FE) model simulating composite laminates with different ply orientations. By comparing the proposed method to a damage detection approach in the literature, the superiority of the proposed method is demonstrated.
Two-Dimensional Acoustic Emission Source Localization on a Laminated Veneer Lumber Plate by Gaussian Process Regression
Acoustic emission (AE) source localization is an important part of monitoring the health of infrastructures. Though straightforward for isotropic materials, where analytical solutions exist, locating sources for anisotropic materials is complicated due to the angle-dependent wave velocity. The problem is even more intractable if heterogeneity involves. This is the case for laminated veneer lumber (LVL), an engineered wood material composed of multiple thin layers of wood, where defects such as knots, voids and discontinuity distributed randomly within the layers, greatly undermining the effectivity of common localization methods for man-made composites. To avoid the problem of heterogeneity, this work employed Gaussian process regression (GPR) to address the 2D AE source localization problem in an LVL plate. With four AE sensors attached at the corner of ROI in the plate, multiple pencil lead break tests were conducted to collect the difference of time of arrival (dTOA) between different sensors. The vector of dTOAs serves used as the input of the Gaussian process regression while the output is the source location. The marginal likelihood was maximized to achieve the optimal model parameters. The input vectors of a different combination of dTOA components were fed into the GPR model, both the predictions on grid points and off-grid points were analyzed. The high accuracy of the mean predictions over all possible combinations of dTOAs indicates such a method can well cope with 2D source localization problems in LVL structures.
Piece-wise Ritz Analysis of Beams Subjected to Discontinuity in Slope
This study presents a Ritz-type analysis for obtaining the vibration frequencies and mode shapes of beams that have a discontinuity in slope. Such structures have a wide range of applications in engineering as they often represent structural idealizations of complex structures such as robotic arms, crack modeling and foldable wings of aircraft. In the present study, the beams have been modeled using Euler Bernoulli’s theory, and the discontinuity in slope is represented by a torsional spring connecting the two segments of the beam. The beams are discretized into subdomains based on the location of the discontinuity. Legendre polynomials are used to define the displacement variation over individual uniform domains. The continuity of displacement is applied at the interface of adjacent subdomains and is enforced into the global system of equations using a condensation procedure. This procedure eliminates the dependent Ritz constant obtained from the displacement continuity. This study focuses on obtaining the vibration frequencies and mode shapes of a simply-supported beam with a discontinuity in slope and compares the results with the analytical solution. The paper would be of interest to researchers involved with the structural health monitoring of beams with cracks, robotic arms, and vibrations of folding wings like the one being considered for 777-X.
Long-Period Dynamic Characteristics of Embedded Track Using Machine Learning
Different from traditional fastener systems, embedded track is a rail placed in the groove and wrapped by a variety of polymer materials, thus realizing the longitudinal continuous support and vibration noise reduction. Due to its superior dynamic characteristics, it has been initially used in trams, subways and high-speed railways. With the promulgation of the Noise Law, its demand is also increasing. However, its structure and mechanism are relatively complex, and its dynamic characteristics changes with the service life. In addition, its performance is difficult to measure directly and service life is as long as 30 years or more. In order to analyze the dynamic characteristic changes of the embedded track throughout its life cycle, fatigue tests are performed by subjecting the embedded track to sinusoidal excitation of different amplitudes and periods. This allows to simulate its service process during the whole life cycle. Meanwhile, the vibration response of embedded track at different stages is collected. Unfortunately, it is difficult to judge the performance and state of embedded track according to the vibration response directly. In order to solve this problem, this paper proposes an embedded track long-period dynamic response analysis method based on machine learning. This method can evaluate the performance change of the embedded track without any label only based on the dynamic response. Among them, self-supervised deep learning networks are used to autonomously extract deep features of the vibration response. These features are then classified by clustering algorithms into different phases of the service life. Finally, the change law of vibration and noise performance of embedded track in different stages is explored. The proposed method can determine the performance status of the pre-embedded track based on the field vibration response test results. It also estimates the decay process of track performance with service life and determines the maintenance cycle according to the performance requirements.
Real-Time Crack Detection in Bridges Using Monitoring and Machine Learning—Verified with an Actual Damage Case
Detecting damage in bridges that present signs of deterioration or have exceeded the expected lifespan is critical for ensuring safety in service. This paper suggests an approach for real-time damage detection for such bridges through monitoring and machine learning algorithms, which serve as timely alarms for decision-making and subsequent damage identification. The approach involves five steps: monitoring, data collection, data separation, feature extraction, and anomaly detection in real-time. Monitoring is ensured by strain gauges, accelerometers, and a temperature sensor. Data collection is ensured at high frequency continuously to capture the dynamic effects of loading. Data separation is provided to classify monitoring data according to loading events, which is in the case of the study characterized by the bridge opening, the bridge closing, and train passages. Feature extraction is provided to characterize monitoring data for each loading event. Anomaly detection is performed by the Isolation Forest and the One-Class Support Vector Machine algorithms. The algorithms are implemented in real-time for each new event. The approach is illustrated in a full-scale post-damage case study of a steel-basculerailway bridge, in service since 1916, with signs of corrosion and fatigue. The results demonstrate the ability of the approach to capture a cracking event in real-time. The Isolation Forest algorithm is found to be more robust for damage detection compared to the One-Class Support Vector Machine. It assigned high scores to the events occurring during and after the cracking, highlighting its ability to capture such incidents promptly. These findings have significant implications for bridge owners as they can identify damage in components in real time, enabling them to take timely measures such as traffic interruption and subsequent repairs.
A Novel Numerical Approach for Fatigue Load Emulation of Offshore Wind Turbines Using Machine Learning
During their lifetime, offshore wind turbines are subjected to complex loading conditions. Understanding the loading history of the support structure of these structures and the long-term implications on the structural integrity renders structural health monitoring (SHM) imperative. In that direction, it is common practice in the wind turbine industry to use only a few physical sensors in a small number of intelligent turbines per fleet to directly measure the load levels in the support structure, implying SHM insights limited to only the instrumented turbines. This often leaves stakeholders with insufficient information on a farm level, not facilitating important decisions regarding project lifetime or adjustments to the trade-off between energy harvest and fatigue damage accumulation through upgraded controller behavior. To bridge this gap, machine learning models have been used as surrogate models with the purpose of virtually sensing fatigue loads through a process known as load emulation. However, previous applications have used site-specific, position-specific training data to develop the machine learning models. As such, the resulting surrogate model decreases in performance when asked to extrapolate to a different site, or even to a different position of the same wind farm. This research presents a novel methodology for constructing a single machine learning load emulation object that is useful for emulating fatigue loads in a generic concept, applicable to any wind turbine within a wide, pre-defined solution space without loss in accuracy. The methodology introduces the use of simplified structural models which preserve key degrees of freedom of the geometric and dynamic properties to represent potential offshore wind sites that cover the desired solution space. These structural models are used to run 10-minute, hydro-servo-aeroelastic numerical simulations and the results are used to train the machine learning model. Load emulation is performed at the foundation-tower interface level. Finally, the surrogate model is validated by comparing the emulated loads from the novel simplified approach against the detailed loads from the numerical assessment of site-specific turbine models at two offshore sites in the North Sea.
An Autonomous Early Warning System Concept for Real-Time Remote Monitoring of Critical Structures
Over the years, Structural Health Monitoring (SHM) and related concepts that focus on monitoring & managing the health of critical structures have gained vast popularity, especially considering the rapid growth and implementation of industry 4.0 approaches in the industrial manufacturing, production and operational environments. However, the implementation of SHM, which is clearly defined by the industry pain-points, have not risen on-par with the expectations of the end users. We believe that this gap could be associated with the following key points, a. the challenges in collecting, handling and analyzing the data collected from the sensors used for monitoring the health of these structures; b. the lack of SHM implementation concepts that establish an added value in rapid decision making and c. the lack of clear V&V approaches that establishes the technological maturity. Although the currently available state-of-the-art sensors are capable of collecting and communicating the sensor data, the complexity in data handling on the edge in combination with selection of the appropriate communication protocol hinders its value for industrial implementation. In addition, in a lot of cases, we observe that the available solutions for remote sensor connectivity do not correspond to the power and connectivity requirements of the end-user. This establishes the need for revisiting the implementation concept of SHM in the upcoming digital future. In this paper, we propose an autonomous early warning system based on smart sensors and the Internet of Things (IoT) for real-time remote monitoring of critical structures. We address the challenges associated with collecting, handling, and analyzing data from sensors used for structural health monitoring (SHM) and highlight the need for a digital, real-time, and reliable sensor solution. Our approach uses novel intelligent sensors, developed and provided by IPR as part of our joint collaborative effort, with edge-computing and an efficient and low data rate (due to data pre-processing and compression into meaningful “digests") wireless communication infrastructure for data collection and new methodologies for data integration & data analysis. We elaborate on the relevant pain-points faced by end-users across several industries, including infrastructure, aerospace, railway, and marine. We describe our autonomous early warning system concept, focusing on its technical capabilities and how it compares with the needs and requirements of end-users. We also discuss our V&V approach, which focuses on the added value of cross-industry innovation and aims to generate innovative solutions and new business cases for SHM across industries. Overall, we believe that our pain-point first approach and our proposed concept can generate vast amounts of experiences and data necessary to establish SHM as a fundamental and necessary concept for estimating the structural integrity of criticsl sltructures. By leveraging smart sensors and the IoT, we can provide real-time monitoring and early warning systems that can help prevent catastrophic failures and improve the overall safety and reliability of critical infrastructure.
Smart Structural Health Monitoring for High-Speed Railway Bridges
The management of ageing civil infrastructure represents one of the most pressing challenges of modern societies. The last Infrastructure Report Card of the American Society of Civil Engineers (2017) estimated that 39% of the more than 600.000 bridges in USA are over 50 years old and estimated the rehabilitation costs at $123 billion. A similar picture emerges across Europe. Spain counts more than 2.606 km of high-speed railway, and a total of 207 critical bridges. An important number of them presents of one form of degradation or damage. The replacement rate is very low and that means an increase of ageing bridges in the next years. Despite large financial efforts have been dedicated to research in this field, the methodology has not been successfully completed/integrated, and life-extension of bridges is still sparsely implemented in practice. The last report of the European Joint Research Centre warned about the weak link between research and the wide-scale adoption of Structural Health Monitoring (SHM) technologies. The economic efficiency of the bridge is the total life cycle cost divided by its service life. New alternatives such as reliability-centered maintenance and reconditioning programs play an ever increasingly important role. However, these improvements are just the first glimpse of an ambitious and promising opportunity: bridge life-extension. To address these deficiencies the present methodology involves important advances on the real application of: SHM methodologies for high-speed railway ageing infrastructures, smart sensors, energy autonomy of the long-term monitoring system, and structural prognosis based on data-driven decision making.
Wireless Strain Monitoring System on the Hernando de Soto Bridge During Critical Repairs
The article discusses the use of a wireless structural health monitoring system supplied by Resensys, after a major fracture was discovered in the tie girder of one of the arch span bridge units on the Hernando de Soto Bridge. The bridge is significant to the US transportation system and the business community in Memphis, as it connects Memphis to Arkansas and most western states in the US. The fracture discovery resulted in an immediate shutdown of the bridge, causing significant economic impacts due to traffic rerouting and massive congestion. According to multiple reports and studies, the cost of closure of the bridge was estimated to be $2-3 million per day. Due to the high urgency of the repairs, monitoring and instrumentation had to be done quickly and before conducting repairs on the tie girder. The Resensys SenSpot™ [1] strain gauges were a perfect solution, providing reliable, precise, and real-time data (strain and temperature) on all high-risk members. The wireless strain gauges were attached to all high-risk members to monitor loads during the repair process, which involved applying tension to the fractured members. Furthermore, the strain gauges monitored strain/stress in critical members when heavier equipment such as cranes were moved on the bridge to carry special equipment and retrofit plates. The Resensys monitoring system provided data to help the bridge repair engineers gather information around real-time strain, and studying stresses and strains on critical elements caused by thermal effects and live loads has helped to ensure that the repair process was effective. Due to the size and other complexities of this project, wiring systems and their associated installation on structures were cost prohibitive. Resensys wireless monitoring system provided numerous benefits in the project which made the implementation of the monitoring program cost-effective and practical. These advantages include longevity, fast and easy installation, cloud-based monitoring, superior sensing, advantageous in construction sites, reusability, etc. Wireless SenSpot™ sensors use non-rechargeable batteries with a battery life of ten years. SeniMax™ [2] remote communication gateways are solar powered. As a result, the complete system is energy self-sufficient and does not require access to external electrical input or infrastructure for its operation.
Technology Innovation in Developing the Health Monitoring Cloud Platform for Maglev Vehicle-Suspension-Guideway Coupling System
The maglev transport system, which has the advantages of a wide speed range, smaller turning radius, and higher climbing ability, has currently been constructed and developed in China. Currently, one high-speed maglev business line and two low-speed maglev business lines are in operation, while many more maglev lines are in construction in China. For monitoring the operation condition of the maglev system, a health monitoring (HM) system based on the cloud platform has been designed for the whole maglev vehicle-suspension-guideway system. The study presented in this paper summarizes the innovative technology on design and the implementation of this HM cloud platform. In this paper, the modular design of the HM cloud platform is introduced first. The integration of on-board and way-side monitoring, and the application of the Big Data technologies, cloud computing, and artificial intelligence algorithms in the HM cloud platform are the focus of this paper and followed by the introduction of the special function of the presented HM cloud platform. The latter is used to verify and evaluate the effectiveness of the suspension control system.
Effects of Thermally Induced Distress on Adhesively Bonded Piezoelectric Wafer Active Sensors and Implications on Damage Detection and Localization
Structural Health Monitoring (SHM) systems enable informing on structural health on a continuous time basis, resulting in optimized maintenance and reduced risk of failure. However, airborne equipment is commonly subjected to harsh environments including extremely high and low temperatures, transient loads, humidity, or radiation. As a result, the sensing function gradually degrades, ultimately impairing the performance of the SHM system to detect and localize damage. In this study, we analyze through two numerical experiments the effects that degrading bonding PWAS interface may produce on electromechanical impedance measurements and guided wave propagation testing. The first experiment investigates the effects of degraded adhesive bonding on electromechanical impedance and guided wave emission using a Finite Element Model (FEM). The results demonstrate that PWAS debonding modifies the electromechanical response, producing considerable distortion on the generated and received wave fields. The alteration of the bonding layer affects the wave tuning curve, resulting in non-uniform wave amplitudes across the plate. The second experiment focuses on the impact of PZT bonding degradation on damage localization. A circular sensor layout with multiple PWAS was considered. The FEM is solved for both intact and damaged plates, as well as for partially bonded PWAS. Tomographic imaging algorithms, including RAPID and Delay-and-Sum (DAS) were used. In summary, this study emphasizes the significance of understanding the effects of degraded bonding in PWAS for a more accurate assessment of aeronautical structures.
Novel Means and Ways of Getting Structural Health Monitoring into Application
Research and development of Structural Health Monitoring (SHM) has been around for decades now. However, with so much effort performed in the past, much more SHM application would have been expected. Reasons for this situation are various but one is certainly the diversity of subjects and topics in which SHM plays a role and vice versa. A way on how to grasp the diversity in SHM and to get it focused to some degree is to get the application of SHM to be shown based on demonstrators. SHM demonstrators is not something new. However, what often happens to those demonstrators is, that they are not used once the project they have been funded from has been completed. In addition to this, the demonstration usually turns up during the end of a project and this maybe even happening in a rush. This short period of time does often not even allow the outcome of the demonstrators to be sufficiently communicated with the true potential applicants, which may be an industry allowing the SHM technology to be commercialized. An initiative has therefore been started out of Germany recently with international participation from Greece, Poland, South Korea and the USA, where available SHM demonstrators are extended in their use, such that data obtained from those can be shared with an international community or data can be generated, validated and disseminated with any SHM technology potential applicants want to get linked to the demonstrator with. So far 14 potential demonstrators ranging from aerospace over wind energy to civil engineering and others have been identified. Within this paper the different demonstrators available so far are briefly introduced including their potential with data already available and the options for SHM technology providers, to get their technology applied, validated, and demonstrated in future actions. The network and hence platform for this SHM technology demonstration initiative will be generally opened to the general public and it is intended to demonstrate the application of SHM technology at various future scientific and technology trading events, would this be traditional conferences, symposia, fairs and possibly others.
A Non-Parametric Mixed Learning Technique for Mitigating Environmental Effects on Structural Modal Frequencies
Health monitoring of civil structures via machine learning is a powerful approach to the early detection of any damage pattern. Besides structural damage, also environmental and operational variabilities are known to affect the inherent structural properties. Although the induced variations in the monitored properties are not harmful, their confounding influence can lead to economic and human losses. For these reasons, a novel unsupervised learning strategy is here proposed, aiming to properly account for the environmental effects on the structural modal frequencies. The offered solution is a non-parametric mixed learning strategy resting on hierarchical clustering, local nonnegative matrix factorization, and Mahalanobis-squared distance (MSD). By means of the hierarchical clustering, training data consisting of modal frequencies relevant to the undamaged condition are subdivided into local clusters, which are then exploited in order to get rid of the environmental effects. The reconstructed data are finally used to train a non-parametric novelty detector based on the MSD, to obtain scores for decision making regarding the current state. To validate the proposed method, a set of modal frequencies of a steel arch bridge in its long-term monitoring has been considered; results show that the proposed methodology is effective in taking aside the environmental variability from the time history of the collected modal frequencies of the structure.
Condition Monitoring of Steel Truss Bridge Using Acceleration Data
This study investigates the efficacy of utilizing data-driven techniques to continuously monitor the condition of steel truss bridges. Rather than relying on computationally intensive modeling methods, this approach utilizes parameters derived from raw acceleration time history data of on-field vibrations caused by regular traffic. Specifically, the study examines the relationship between changes in these parameters and the occurrence of damage in steel truss bridges. To conduct this research, the Pamban Bridge, which is over 100 years old, was fitted with accelerometers at various bottom node points on the bridge. The acceleration data was then parameterized into primary, secondary, and tertiary order parameters based on amplitude, frequency, and duration. These parameters were then analyzed to determine their suitability for detecting damage. This study examines the continuous variations in each parameter over an identical duration from March to July 2021 and March to July 2022, spanning 136 days each and over 1000 train passes. The bridge underwent retrofitting during the intervening period. A linear best-fit line is found for each sensor reading for the considered duration. The slope and intercept of the linear fit are studied. It was found that the change in the intercept values indicated the changes that occurred consistently and reflected the expected trends between sensor locations. To further validate the findings, the observations made through these parameters were compared with the retrofitting data of each member. The parameters, such as root mean square acceleration, arias intensity, characteristic intensity, and cumulative absolute velocity, were consistent and exhibited similar patterns during the observed period. Overall, this approach to condition monitoring is highly efficient and requires only on-field vibrations caused by regular traffic to detect potential damages in steel truss bridges, making it an ideal method for continuous monitoring without operational downtime.
On Condition Monitoring of a Corroding Steel Truss Bridge – A Case Study
A railway steel truss bridge connecting the mainland India with Rameswaram had corrosion issues. This bridge was continuously monitored from August 2020 through December 2022 to ensure the serviceability of the bridge. This paper summarizes the technical learning from the monitoring. The truss bridge was heavily instrumented – 84 out of 92 members were instrumented with linear electrical strain gauges, 20 of the 52 nodes had biaxial accelerometers, and 10 resistance-based temperature sensors. For each train pass, the collected data was analyzed to estimate the axle loads of the train, cross-sectional areas of the members, and certain other parameters from the acceleration time history. While the observed strains could be matched excellently with the strains estimated by simple truss analysis in 70 members, the computed accelerations were the order of magnitude lower for all the acceleration measurements. The displacement profile obtained by double integrating the acceleration with appropriate filtering, as suggested in the literature, did not agree with the predicted displacement even qualitatively. As one would expect, even though there was a steady deterioration of the structure, the commonly used dynamic response parameters – the natural frequency and mode shapes – did not reflect the change statistically. The extreme strains in most of the members showed a steady increase or decrease in values. But the truss being statically determinate, the strains could not capture the loss in the area of members due to corrosion at locations other than where they were located. However, certain frequency and time domain parameters of the acceleration were able to capture the steady deterioration of the truss bridge. Thus, in the case of statically determinate truss bridges, data-driven approaches seem to perform better than physics-based approaches in determining the changes occurring in the bridge.
Impact of Spectral Shift Quality (SSQ) on Fibre Optic Sensor Readings in Reinforced Concrete Beams
Efficient maintenance and health monitoring programs prevent irreversible damage to critical civil infrastructures and save millions of dollars of taxpayer’s money. The precision of the sensors and the volume of the experimental data collected are extremely important factors in the actual testing of structural health monitoring systems. Distributed fibre optic sensors (DOFS), currently being used extensively in civil structures for structural response measurements. However, DOFS suffers from exceeding its measurable range of strain when applied to concrete structures after initiation of cracks which cause erroneous data in the data stream. This phenomenon can be identified by using spectral shift quality (SSQ) levels of the measurements. Therefore, an experimental plan was proposed with representative size reinforced concrete (RC) beam to study the effect of SSQ on DOFS (SMF 28) readings. A RC beam with a dimension of 4000 mm × 200 mm × 400 mm was cast with embedded DOFS rebar sensor for the tension side. After the curing period, DOFS surface sensor was attached to concrete bottom surface. The beam was tested under flexural loading up to a maximum load of 160 kN until the beam failure. Four-point bending arrangement was used to test the beam and the measurements were taken in 10 kN intervals by using an optical backscattered reflectometer (OBR 4600). The SSQ should theoretically have a value between zero and one. The manufacturer of OBR 4600 recommended to remove any data with an SSQ of equal or less than 0.15, and the same threshold was applied in the study. Due to the crack initiation on bottom surface, SSQ reached 0.15 threshold after 40 kN load and for subsequent loadings for bottom surface sensor. This means that the sensor can be utilized until a load of 40 kN, which is 25% of the maximum load. However, experimental findings reveal that rebar attached DOFS SSQ values are within an acceptable range until 80% elastic limit strain. The rebar sensors functioned until 120 kN of loading, or 75 % of the maximum loading. Consequently, it can be noted that DOFS operate well with rebars and that SSQ had no effect on the results until 80% of elastic limit. However, the influence of SSQ on the concrete surface sensor was notably substantial. Therefore, it is warranted to pay a closer attention to the DOFs sensor readings after formation of hairline cracks on the attached concrete surfaces.
Performance of Crossed Long-Gauge Strain Sensors in the Spread Footing Foundation
Estimations of shear stresses are essential design procedures of spread footing foundations and detecting shear strain at critical planes is crucial in structural health monitoring because it provides valuable insights into the structural stability of the foundation. Crossed topology of long-gauge strain sensors can be used to monitor shear strain. By an ideal layout of two strain sensors in a planar case, the effect from the normal force, bending moments, and even temperature gradient can be eliminated. However, some slight position changes, e.g., due to on-site conditions, may affect the performance of crossed sensors. Therefore, it is necessary to study the influence of imperfections in the geometrical position of sensors on the measurements of crossed sensors. This paper presents a study on the performance of long-gauge sensors embedded in a spread footing foundation of a five-floor garage at Princeton University. We investigate the effects of topology on the measurement of average shear strain and the relationship between the evaluated shear and loading from the superstructure. The results show that the imperfections in the positioning of sensors have a significant impact on the measurement of shear strain, which needs to be taken into consideration when interpreting the sensor data. The average shear strain evaluated using the crossed sensors measurements is compared with theoretical values obtained from structural analysis. The results validate the estimated relationship between detected strain and applied forces. The findings of this study have important implications for the use of crossed long-gauge strain sensors beyond spread footing foundations, as the method applied here can be extended to other types of structural elements.
Static and Dynamic Bridge Monitoring with Distributed Fiber Optic Sensing
Structural health monitoring (SHM) of bridges and other critical infrastructure is progressively gaining more importance, especially since many bridges in Central Europe and North America are coming to the end of their design life time. Efficient monitoring is therefore necessary to ensure safe operation. In order to measure not only fast changes due to traffic loads but also slow changes caused by temperature or degradation, several different sensor types are usually needed. Distributed Fiber Optic Sensing (DFOS) can provide both static and dynamic information. This article focuses on the monitoring of a steel-concrete composite highway bridge in Austria. Two of the seven spans were equipped with fiber optic sensing cables. By using different DFOS techniques such as Distributed Acoustic Sensing (DAS) and Brillouin Optical Frequency Domain Analysis (BOFDA), both dynamic and static monitoring can be realized seamlessly along the sensing cables. The combination of both fiber optic measurement techniques allows the determination of the vibration behavior of the bridge where not only the eigenfrequencies can be observed, but also slow changes caused by external influences such as temperature. We demonstrate that individual vehicles can not only be identified in DAS measurements, but also characterized in terms of mass and speed. To verify the static and dynamic DFOS results, point-wise sensors such as Robotic Total Stations (RTS) and accelerometers are used as reference.
Integrating Pre-Existing Telecommunication Fiber Cable Vibration Sensing and Drive-by Vehicle Vibration Sensing for Scalable Bridge Health Monitoring
We introduce a novel and scalable bridge health monitoring approach by combining pre-existing telecommunication fiber vibration sensing and drive-by vehicle vibration sensing. Many bridges have pre-existing telecommunication fiber cables crossing in conduits, which can function as a distributed acoustic sensing system to continuously record bridge dynamic strain responses in high spatiotemporal resolution. In addition, the drive-by vehicle vibrations capture the information of the input load to the bridge and the interactive bridge vibrations with high spatial resolution. Our approach integrates the two sensing systems to offer complementary input-output information and insights into bridge conditions. It estimates bridge modal properties by modeling the vehicle-bridgefiber interaction with an augmented state-space model. The model considers the bridge dynamics and input loads as a joint state observed by the telecommunication fiber vibration and drive-by vehicle vibration sensing systems, respectively. Our approach is scalable as it does not require on-site installation and maintenance of sensing systems on the bridge. It is also more accurate than using a single sensing system because it takes into account both the input and output signals of the system being analyzed. We evaluate our approach with real-world experiments on a steel stringer/multi-beam bridge in Palo Alto, California, with telecommunication fiber cables running under the deck. Our approach successfully estimates bridge dynamic properties, including natural frequency, damping ratio, and mode shape. It outperforms a baseline method that only uses telecommunication fiber vibrations, which indicates the potential for accurate and scalable bridge health monitoring.
Vibration Propagation Analysis of Road Pavement Using Thin Layer Method for Fiber Optic Distributed Acoustic Sensing
Distributed Fiber Optic Sensing (DFOS) has gained widespread utilization as a tool for Structural Health Monitoring (SHM) in recent years. This technology enables the measurement of strain and temperature distributions along a fiber. Among the various DFOS measurement technology, this study focuses on the use of a Distributed Acoustic Sensing (DAS) system, which primarily measures longitudinal strain caused by vibrations. The utilization of DAS for vibration propagation analysis has not been widely practiced. Thus, to confirm the authenticity of the recorded values, we constructed a mockup consisting of multiple layers including a surface layer, base layer, upper layer roadbed, lower layer roadbed, and subgrade, with DFOS installed at multiple locations in each layer. We are trying to improve our modeling by comparing the analytical road model to the DAS measurement results. When pavement is subjected to vibration, caused by either people or vehicles passing by, it can be expected that the amplitude of the vibration will decrease as the depth increases. However, when we measured the vibrations using DAS, we observed that the amplitude of the surface layer and the subgrade was larger than that of the other layers. Therefore, we applied the thin layer method, which is widely used in the field of geotechnical engineering to investigate the factors that led to the increase in amplitude of the subgrade. In the thin layer method, the ground is considered a homogeneous continuum in the horizontal direction, while the depth direction is analyzed discretely in the plane of layer partition. This method has a smaller computational load than the finite element method. We performed sensitivity analysis by using shear wave velocity, Poisson’s ratio, and density as input parameters. The sensitivity analysis revealed that changes in shear wave velocity strongly contributed to the amplitude. To validate the analytical values obtained from the thin layer method, we conducted the Falling Weight Deflectometer (FWD) test, which provided us with the actual physical properties of the road pavement, including the deflection of the surface layer under 5t excitation forces. We reflected these results into the thin layer method’s parameters and compared the analytical values with the DAS measurement results of the exciter test. Through these procedures, we confirmed the suitability of the thin layer method as an analysis method for DAS.
Topological Data Analysis for Real-Time Extraction of Time Series Features
Real-time state estimation is critical in rapidly assessing structural health to empower real-time feedback mitigation strategies. Of interest to this paper is the state estimation of high-rate system dynamics. High-rate systems are defined as dynamic systems experiencing high-rate (< 100 ms) and high-amplitude (acceleration > 100 gn) events. Examples include hypersonic vehicles and active impact mitigation strategies. The advanced operation of these mechanisms can only be achieved through control and feedback systems capable of operating in the sub-millisecond range, thus necessitating tight performance constraints. Additionally, high-rate system dynamics are highly nonlinear and non-stationary, for which traditional real-time inference methods cannot provide accurate predictions. Topological data analysis (TDA) is gaining popularity for classifying complex time series. Its integration with architected machine learning algorithms shows promise in advancing the predictive capabilities for high-rate systems. This paper investigates the use of TDA features in conducting state estimation. Some TDA features are explored on a physical perspective, and their applicability to the high-rate state estimation problem is assessed. A promising TDA feature is selected, namely the maximum persistence of Hð, and applied to laboratory datasets extracted from the dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR) testbed. The task consists of detecting the location of a fast-moving boundary condition on a cantilever beam. Results demonstrate that the feature can be used to detect the location of the moving boundary condition online. A discussion on real-time location of a fast-moving boundary condition on a cantilever beam and the applicability to high-rate systems is provided.
Microsecond Model Updating for 2D Structural Systems Using the Local Eigenvalue Modification Procedure
Systems that experience high-rate dynamics, such as blast or impact, are susceptible to rapid alterations that could result in loss of life and financial investments. These systems are characterized by a high dynamic response with a high-rate (< 100 ms) and high amplitude (> 100 g³). A system exposed to high-rate dynamic environments is frequently prone to rapid plastic deformation, which can cause structural, electrical, and sensor damage. A feedback loop of fast-acting actuators empowered with rapid state estimates can be utilized to stop further harm. The state estimator must be quick and resilient to the significant uncertainties, non-stationarities, and strong disturbances associated with high-rate dynamic systems. A model for 2Dimensional systems is developed to demonstrate high-rate tracking or estimation of a structure where a change in stiffness at locations on the system represents damage. The Local Eigenvalue Modification Procedure (LEMP) algorithm is applied to solve the system’s equation quickly and efficiently within a set latency for state estimation. LEMP utilizes a single generalized eigenvalue solution for the initial system and simplifies altered state equations by transforming them into modal space, isolating the DOFs that contribute to the changes between states, and defining equations in terms of the initial state, thereby reducing computational time. This preliminary work develops a 2D finite element model using classical plate theory. A 2D model simulation of the plate’s initial state is carried out on Abaqus and compared to the analytical model formulated solved using the generalized eigenvalue approach to test the formulated model. The changes made to the plate are then solved using LEMP to avoid solving the time-consuming eigenvalue solution. In this work, the change in the system is demonstrated by change in stiffness at different locations on the plate. Results report the performance metrics for the considered case. The approach’s applicability to deployment on edge computing systems for real-time model updating of structures operating in high-rate dynamic environments is discussed.
Bayesian Damage Estimation with Regularized Data-Driven Stochastic Time Series Model
A probabilistic vibration-based global SHM technique is proposed. In the process, experimental data from a modal test on a wing structure is used to identify a unified model with i) a Vector-dependent Functionally Pooled (VFP) component, ii) and an Auto-Regressive eXogenous (ARX) component. LASSO regularization is incorporated as a model structure selection method while introducing model sparsity. A probabilistic damage identification/quantification method within a Bayesian architecture is applied to solve the inverse problem, which provides a decision confidence interval for damage estimation.
A Data-sparse Approach to In-situ Fault Detection and Identification for Metal Additive Manufacturing
With increasing adoption of metal additive manufacturing (AM) in manufacturing, detecting faults in the printing process has the potential to reduce waste from failed prints and streamline the production process. To increase robustness of anomaly detection, a statistical method of detecting faults from melt pool images is presented. This method uses parametric identification of 1D compression of melt pool images to build a nominal predictive model. Nominal melt pools result in residuals that are Gaussian white noise processes, whereas anomalous melt pools will not follow this distribution. Detection is performed through statistical comparison of incoming data with a nominal reference generated on sparse data. This approach successfully applies statistical time-series methods to detect anomalous melt pools in a metal AM process.
Online Fatigue Damage Detection for Structures under Random Loading Using a Comparative Sensor-Data Approach
Fatigue failure of metallic structures such as wind turbines towers or steel bridges is a problem that affects their remaining service life. The current industry standard of fatigue damage assessment is based on time domain cycle counting to estimate the cumulative fatigue damage which is compared to a reference value to detect fatigue failure. The stochastic nature of the fatigue process and the uncertainty in choosing the appropriate reference value limit the applicability of this approach. Fatigue crack detection is the focus of many structural health monitoring techniques. A novel sensor-data comparative approach for fatigue damage detection is proposed in this paper. This approach is based on online monitoring of the linear correlation between monitored and reference sensordata. Fatigue failure is detected when the linear correlation is lost. The proposed concept is validated experimentally and the obtained experimental results prove the possibility of early detection of fatigue failure.
Experimental and Numerical Studies of an Acoustic Coupler for Multiplexing of Guided Waves for SHM
Optical fibers can propagate both lightwaves and ultrasonic waves, as has been recently shown to be useful for guided wave based SHM. Additionally, the ultrasonic waves can be transferred between optical fibers using an acoustic coupler. This paper discusses the role of geometrical parameters on the acoustic energy transferred between two cylindrical waveguides, through a bonded coupler. Finite element simulations of the energy transfer are performed for different parameter sweeps.
Effects of Imperfect Interface on the Dispersion Curves of Torsional Waves in a Composite Hollow Cylinder
This paper investigates the influence of interfacial imperfections on the dispersion relationship of torsional waves in a composite hollow cylinder. The imperfect interface between the inner and outer tubes is modelled by a linear spring interface model, where tractions are continuous across the interface and the displacement jump is assumed to be proportional to the tangential shear stress acting on the interface. The dispersion curves of normalized frequency as a function of normalized wavenumber can be determined by solving an eigenvalue problem. It is shown that for a fixed wavenumber, the modal frequency decreases with increasing interfacial spring compliance, especially for smaller wavenumbers. Besides, at larger wavenumbers, the loss of perfect continuity at the interface has a reduced impact. The thickness effect of the outer tube is further analyzed. The computational results indicate that the modal frequency rises in the case of the thinner coating layer. These torsional wave propagation features may provide insights and guidelines for nondestructive evaluation of composite hollow cylinders with imperfect interfaces. The paper finishes with a summary and concluding remarks.
Best Reconstruction Frequency of Lamb Waves Via Broadband Measurement
This article presents the theoretical basis of the best reconstruction frequency (BRF) of Lamb waves. It has been proposed earlier by the author’s group as the probing frequency in the refined time-reversal method for damage detection to achieve enhanced sensitivity and accuracy. It is shown to be the frequency at which the amplitude dispersion of the main mode of the reconstructed signal is minimum. It is then possible to compute the BRF from a single measurement of Lamb wave response under a broadband (e.g., Gaussian) excitation. Thus, it is no longer required to compute the similarity at different central frequencies of the narrowband input. It is further shown that the BRF is a system property of a given transducer-plate-adhesive system, which is independent of the distance between the transducers used for the time-reversal method in pitch-catch mode. This property makes it suitable for use when a network of sensing paths is used to identify damage locations.
Wave-Based Damage Identification in Composite Strips and 2D Solids Using Inverse Multiresolution Wavelet Methods
An inverse procedure for damage identification on 1D and 2D solids based on wave propagation using the multiresolution finite wavelet domain (MR-FWD) method is presented. The forenamed method utilizes Daubechies wavelet and scaling functions for the approximation of state variables and as such, it involves two types of solutions, the coarse and the fine solutions. In that way, the multiresolution nature of the method can be utilized for efficient damage estimation in experimental applications since the fine solutions of the method have manifested remarkable localization and isolation capabilities and high sensitivity to damage. In order to fully take advantage of the additional benefits of the MR-FWD method, full-field displacement measurements of the wave propagation are taken into consideration. Wavelet decomposition using Daubechies wavelets is now applied on the measurements, leading to approximation and detail components that are directly comparable to the coarse and fine solutions of the multiresolution simulation, respectively. Therefore, MR-FWD models can be created using the same Daubechies wavelets as the decomposition of the experimental data, so as to compare the simulation results with the measured ones. Numerical results reveal that comparing the detail component of the experiments with the fine solution of the simulations using appropriate metrics can lead to efficient damage identification. In such manner, an optimization process can be conducted in order to characterize the investigated damage scenarios. This procedure can lead to more sensitive and accurate damage estimation due to the advantages of the multiresolution analysis.
Fiber Bragg Grating Sensor Based Mode Filtering Using Cosine Distance Metric
Fiber Bragg grating (FBG) sensors are considered ideal for structural health monitoring (SHM) due to their small weight and size, ability to be embedded and ability to be multiplexed. So, FBG sensors are commonly used as strain sensors for SHM but their use for ultrasonic guided wave (GW) measurements is not common due to the low sensitivity. In the recent times, a renewed interest is seen in the use of FBG sensors for GW measurements using the edge reflection approach which increases the sensitivity several folds. It has been reported, that the mechanism of the measurement of the incident GW is different based on the relative ratio of the wavelength of the incident GW (GW) and the grating length (L) of the FBG sensor. For ratios GW/L» 1 the propagating wave leads to uniform strain over the FBG resulting in the shift of the Bragg wavelength. For the λGW/L ≈ 1 the FBG experiences non-uniform strain over the FBG which results in the distortion of the spectrum (widening or narrowing) of the peak. By separating these effects on the FBG, mode filtering may be achieved. In the previous work the mode filtering was achieved by scanning the entire reflectivity spectrum of the FBG (over 300 points). This approach is time consuming and hence has limited applicability for in-service SHM. Hence, a novel method which uses only measurements at 2 locations on the spectrum is proposed in this paper. The cosine distance in the waveforms is calculated and then used for identifying the mode. This information is then used for damage localization on a simple plate.
Sensitivity to Axial Stress and Temperature of Local Resonances in Rails
Measuring rail thermal stresses or rail neutral temperature (RNT) in continuous welded rails (CWRs) is a challenging task for the railroad industry, especially in a nondestructive and nondisruptive manner. This paper examines the potential of local resonances for thermal stress measurement in rails. Local resonances associated with zero-group velocity (ZGV) and cutoff frequency points usually demonstrate sharp resonances in amplitude spectra, which can be utilized for Nondestructive Evaluation (NDE) and Structural Health Monitoring (SHM), and we previously reported their existence in rails. In this study, we promote local resonances by bonded piezoelectric elements on a short rail sample. Two tests were performed: (i) the sample is subjected to stepwise increasing uniaxial compressive loads in a constant temperature environment, and (ii) the same sample is free to expand and subjected to rising temperatures in an oven. By measuring local resonant frequencies, we quantified the sensitivity of the resonant frequencies to axial stress and temperature. The results show that appreciable sensitivities of the local resonances are found under varying stress and temperature levels and can be utilized for in-situ rail thermal stress determination.
Structural Integrity Assessment of Nuclear Spent Fuel Canisters Using Guided Waves
Dry storage canisters (DSC) are primarily used for storing nuclear spent fuel rods for an extended period. Due to the critical role of these assemblies, periodic inspections are necessary for mitigating potential accidents and subsequent release of radioactive material to the environment. Aside from visual inspections, the most effective approach to evaluate their structural integrity is by non-destructive techniques. In this work, we utilize helical guided ultrasonic waves (HGUW) to develop a non-destructive scheme for the structural health monitoring of DSC. These waves share identical characteristics with traditional Lamb waves but travel circumferentially in cylindrical waveguides. The proposed methodology is comprised of instrumenting the canister with a permanently attached network of piezoelectric patches (PZT) capable of transmitting and receiving high frequency HGUW. Using the data collected from the HGUW inspection, a probabilistic imaging technique is employed to localize defects within the span of the PZT arrays. Furthermore, an investigation of the scattering of the fundamental Lamb modes from different cracks and notches is investigated and the patterns are exploited for improving the fidelity and accuracy of the imaging method. Experiments have been conducted on various specimens including a 6-in-diameter cylinder in the lab and an actual DSC with a diameter of approximately 6 feet.
Uncertainty Quantification of Thickness Estimation Via Full-Field Ultrasonic Inspection
Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field ultrasonic inspection technique that can be utilized for structural health monitoring applications. ASSESS can rapidly identify damage or defects over a 360o field of view of a structure using geometry mapping and a scanning laser Doppler vibrometer (LDV) – enabling inspection and characterization across multiple large surfaces in a single measurement. However, variables like the incidence angle between the surface and LDV beam change when inspecting large or geometrically complex structures; greatly affecting the signal-to-noise ratio (SNR) of the measurement and the accuracy of damage estimation to an extent that has not previously been quantified. This work experimentally quantifies the uncertainty of thickness estimation derived from full-field steady-state wave fields over various incidence angles to provide trusted operating bounds. A rotation stage and hexapod assembly were used to orient an aluminum plate with manufactured thickness losses to up to 80o both horizontally and vertically in increments of 5o. The plate specimen was designed with an assortment of manufactured damage, including discrete regions with both continuous and discontinuous material thickness loss. An affixed transducer excited the plate with a 199 kHz tone and an LDV recorded local surface response velocities. A LiDAR was used to measure the inspection surface geometry and random sample consensus (RANSAC) planar extraction was utilized to determine the orientation of the specimen with respect to the LDV and correct perspective distortions in the wavefield. This work lays an essential foundation for identifying physical limitations of the measurement system for scanning large and complex structures and providing quantified uncertainties for detected damage within a trusted operating range – an imperative step for adoption of this inspection technique within highly standardized industries.
Detection, Localisation, and Quantification of Bolt Looseness in an Aluminium Plate Using Lamb Wave Analysis
In this paper a method is presented to detect and locate loose bolts. This method focuses on changes in Lamb waves when they encounter a different propagation path, whether that is in the form of stress concentrations, or the intensity of reflection from a change in contact area. Utilising this method, the authors show that a loose bolt can be located for a variety of different torque levels. However, the sensitivity to detecting the extent of the damage proved to show no benefits and further investigation is needed.
The Effect of Temperature on Guided Wave Signal Characeeristics in a Honeycomb Composite Sandwich Structure with Disbond and Delamination
Researchers developing guided wave (GW) based structural health monitoring (SHM) techniques frequently utilize amplitude and group velocity variations to detect and localize damage. However, external factors such as temperature and moisture can affect these features. To address this, a coordinated numerical and experimental study was conducted to investigate GW propagation characteristics in honeycomb composite sandwich structures (HCSS) with two types of damage: disbond and delamination, across a temperature range of 0° to 90° C. Computationally efficient two-dimensional (2D) numerical models were developed using COMSOL Multiphysics that takes into account a variety of temperature-related phenomena, such as thermal stresses and changes in the material properties of honeycomb sandwich and piezoelectric wafer transducers (PZTs). The amplitude and group velocity of the fundamental anti-symmetric (A0) mode are found to increase with disbond and decrease with delamination. Additionally, the normalised amplitude of the A0 mode linearly decreased with increasing temperature for both healthy and damaged cases. Given the A0 mode’s wide usage for damage detection, an adjustment equation for amplitude and group velocity with temperature change is proposed.
Differential Strategy to High-Speed Inspection of Rails Via Passive Ultrasonic Monitoring
This work presents an amplitude-imbalance identification approach to noncontact high-speed rail inspections. The passive monitoring utilizes an array of capacitive aircoupled ultrasonic transducers for data acquisition of ultrasonic-guided waves propagating in the rail. The excitation is generated by a combination of the test car’s wheel-rail friction and white noise generated by two additional actuators. The transfer function is reconstructed passively between two air-coupled sensors and transformed to the time domain by deconvolution. When discontinuities are present in the rail, the signature waveform in the time domain transfer function will change due to scattering. These changes in the amplitude of the reconstructed signal can be associated with the potential presence of a flaw. This method is referred to as the conventional approach to defect detection. However, tracking changes in one waveform is still subject to variability due to changing test conditions, different rail geometries, wear, poorer signal strength, and other rail anomalies that may be falsely flagged as defects. This paper presents a more robust defect detection strategy achieved by simultaneously assessing amplitude-related features from time signals of two sensors pairs probing adjacent segments of rail and using any significant mismatch as the indicator of an internal rail flaw in one of the two segments, hence, the differential strategy for defect detection. The performance of such a method in detecting flaws was assessed via receiver operating characteristic curves on data acquired from field tests at testing speeds of 40 mph conducted at the Transportation Technology Center Inc (TTCI) in Pueblo, CO.
Analysis of Acoustic Wave Generation by Low Frequency Air-coupled Transducers
In this paper phenomenon of acoustic waves generation in the air by low-frequency air-coupled transducers (ACT) is investigated. Numerical modelling of acoustic waves propagation in the air was investigated in the COMSOL Multiphysics software. The propagation of acoustic waves generated by a single ACT was modelled, to consider its angular pressure characteristics. Furthermore, the process of acoustic waves generation by multiple ACTs was investigated. Numerical results were validated by experimental measurements based on the scanning laser Doppler vibrometry using the acousto-optic phenomenon. The results of this research are focused on the elastic wave generation in the structures by ACT and are targeted towards non-destructive testing.
Ray Tracing Methodology for Elastic Wave Propagation Improve Simulation Applied to Aeronautic Composite Structures
Data processing and simulations for guided wave based Structural Health Monitoring (SHM) systems can be extremely challenging, especially in complex geometries. Aerospace structures, specifically, may contain integrated stiffeners, thickness changes and other structural details, which will result in a very complex signal on the sensor network due to the multiple reflection of the multiple modes on the structural elements. Currently, the most common methodologies to simulate Lamb waves are generally based in the finite element method (FEM), spectral element method (SEM). FEM models require a large amount of computational resources and therefore are limited in the size and number of cases that could be calculated, and SEM present some difficulties to implement out of plane reinforcements and to modelize structural details. For this reason, being able to perform a fast and efficient assessment of the propagation of elastic waves can be very helpful to reduce the need of physical tests, and could be used to identify the critical cases to run with a more detailed numerical simulation or to perform an stochastic analysis. In an attempt to solve this problem, this study presents an analytical methodology based on the ray tracing method, able to solve the propagation of symmetric and anti-symmetric elastic waves on a 2D space and able to take into account different material characteristics, propagation velocities and simple boundaries; the methodology considers different propagation modes independently and takes into account the splitting of rays and possible mode changes when encountering a boundary during the propagation. The capabilities of this method are demonstrated on a series of analytical study cases and compared against physical tests, presenting an equivalent or better correlation than the finite element method, with a substantial reduction in the computational cost.
Analyses by Global-Local Method of Ultrasonic Guided Waves Propagation in Pristine and Defective Plates for Accurate Quantitative SHM
The efficient and accurate modeling of ultrasonic guided waves (UGWs) can be a very effective tool to optimize UGWs inspections and enable defect characterization when performing structural health monitoring (SHM). Among the several strategies, hybrid methods provide the capability of representing accurately the hosting structure and defect configurations, while leveraging the decreased computational costs of reducedorder and/or semi-analytical methods. In this work, the Global-Local method is used to simulate UGW propagation in composite plates with defects, to study the effect of the incident mode interaction with the scatterer and its features (size, location, local zone relation). To do so, the evolution of the method is presented to account for the inclusion of evanescent modes and to accommodate the forced solution framework. In the first improvement, the role of evanescent modes will be discussed with respect to accuracy and computational cost considerations, providing guidelines for effective UGW modeling in composite materials. In the second upgrade, the 2D time-space response to different spatial and temporal sources is observed and analyzed in terms of its spectral content, to differentiate pristine and defective conditions, in composite materials with increasing defect severity. These improvements in the numerical framework of the Global-Local approach are significant to enable quantitative SHM and prognostics by accurate and efficient predictions of UGW scattered responses, and to advance defect characterization by UGWs by analyses of UGW data.
Non-Destructive Evaluation of FSW Tool Plunge Depth in Thin Metallic Sheet Weld Using Ultrasonic Guided Waves
Due to their high strength-to-weight ratio, thin aluminium alloy sheets are widely used in industries such as automotive, marine, railway, and aerospace. Welding has been discovered to be an effective method of joining, particularly in comparison to mechanical fasteners and semi-joining processes. However, fusion welding techniques can be vulnerable to hot cracking, which can result in solidification and liquation cracking. For lap welded joints in thin metallic sheets, friction stir welding (FSW) has proven to be very efficient. Non-destructive testing (NDT) methods are utilized to examine weld morphology. The ultrasonic method has demonstrated the ability to measure weld penetration depth in thick metallic plates using bulk and surface waves. However, the tool plunge depth (TPD) in FSW, which is analogous to the weld penetration depth of fusion welding, plays a crucial role in the weld strength of thin structures that has not been investigated using non-destructive methods. This study aimed to predict the optimum tool plunge depth for sound welding in thin structures by employing a guided Lamb wave-based non-destructive method. At a low frequency of 25kHz, only anti-symmetric (Ao) mode was transmitted through low TPD weld and through optimum TPD weld both Ao and symmetric mode (So) was getting transmitted. However, through high TPD weld both Ao and So was getting transmitted as well as extra anti-symmetric (AoAoc or SoAoc) mode was observed due to mode conversion. This different scattering behaviour of Lamb wave signals for varying tool plunge depths was analysed to predict the FSW tool plunge depths with accuracy.
Application of Damage Detection of Steel Structure Lamb Wave Modal Superposition Imaging Based on Scanning Laser Vibration Measurement
Due to their high strength, high-temperature resistance, good toughness, and plasticity, steel structural plates are widely used in major engineering structures such as high-speed rail, vehicles, and civil engineering. However, they are easily damaged by external loads and impacts during long-term service. Aiming at the shortcomings of dispersion and multi-modal effects in the current Lamb wave detection, and since damage information cannot be effectively identified, this paper studies, through simulation and experiments, the Lamb wave driven by a sinusoidal modulation five-peak wave signal in a steel structure plate with flat-bottomed holes. The finite element numerical model of the propagation was established, and the corresponding damage detection experimental platform including steel and aluminum boards was built. The experimental data were obtained by Scanning Laser Doppler Vibrometer (SLDV). It can be seen from the experimental results that under the condition of the same frequency, the damage reflection energy of the two different modes is relatively obvious. Through the data fusion of the two modes, the results obtained are more accurate than the damage imaging results of the single mode in the frequency wavenumber (f-k) domain. Finally, the damage location experiment of one through hole in the steel board and the double flat bottom holes in aluminum plate are carried out to verify the feasibility of the algorithm in isotropic plate.
Active Sensing Using LDV and T-Shaped Arrays in Metallic Plates for Damage Localization
In this work, T-shaped arrays of sensors generated by a single laser Doppler vibrometer (LDV) are used for damage localization in a metallic plate. Previous studies have used a single T-shaped array of piezoelectric patches to estimate the direction of arrival of waves in plate-like structures. Here, by comparing the measurements of two and four T-shaped arrays with baseline information, this technique could be extended for damage localization, resulting in an active sensing application. Instead of piezoelectric transducers, a LDV is used to create the T-shaped arrays, which enables the testing of arrays with different numbers of measuring points and different positions. The difference between test and baseline measurements is used, as it contains information on waves from a known acoustic source that scatter and reflect at a damage site. Beamforming is then applied to these residual signals, and the resulting beamforming maps are transformed into a map of positions in the inspected area. The measurements of each array results in one position map, and combining all maps results in an estimated location of the damage. This is demonstrated in a square metallic plate with a single source of vibration located at its center, while the arrays are located at the boundaries of the inspection area. The tests show that even for low density arrays, increasing the number of arrays enables damage detection with reasonably good results, even when employing simple delay-andsum beamforming instead of more advanced and complicated processors. This work also serves as a proof of concept that a set of T-shaped arrays can be reliably used for active sensing of damage in plane plates.
Wave Propagation and Damage Localization in Thick-Walled Hollow Cylinders Through Inner Sensing
Thick-walled hollow cylinders (TWHCs) are widely used in engineering structures and transportation systems, exemplified by train axles. The real-time and online health monitoring of such structures is crucial to ensure their structural integrity and operational safety. While elastic-wave-based structural health monitoring (SHM) shows promise, the development of feasible methods strongly relies on a good understanding and exploitation of the wave propagation properties and their interaction with structural defects. TWHCs usually bear multiple wave modes, which is a less investigated and explored topic as compared with thin-walled structures. This work examines this issue and proposes a dedicated damage localization strategy by using the selected waves captured on the inner surface of a TWHC. It is shown that, alongside the quasi-surface waves on the outer surface, longitudinal waves converted from the thickness-through shear bulk waves are generated to propagate along the inner surface. Their propagation characteristics are exploited for damage localization based on hyperbolic loci methods through inner surface sensing. Numerical studies are conducted to validate the method, alongside experimental verifications on a benchmark TWHC containing a notch-type defect. Studies provide guidance on damage detection in TWHCs and sensor network design.
Ultrasonic Guided Waves-Based Nonlinear Autoregressive Defect Detection for Railway Tracks Using Fiber Bragg Grating Sensing
Structural health monitoring (SHM) is crucial to the maintenance and daily operation of civil infrastructures. Railway system, which plays an important role in modern society, relies heavily on robust monitoring systems to give timely warnings of early-stage defects that potentially could result in the consequences of major traffic incidents, such as derailments. Guided wave testing (GWT) methods have been introduced into the rail track monitoring, featuring long-distance monitoring reliability, high sensitivity, and excellent efficiency. In recent yea rs, the deployment of optical fiber-based GWT on railway system has prevailed traditional piezoelectric sensingbased schemes, due to its reliable performance especially under high electromagnetic interference (EMI) environments. In this paper, experimental studies are conducted, where fiber Bragg grating (FBG) sensors are utilized to receive ultrasonic guided waves (UGWs) on railway tracks, induced by a lead zirconate titanate (PZT) sensor, to detect defects. A narrow-band laser is induced to conduct edge filter demodulation of ultrasonic FBGs, with the sampling frequency of 10 MHz. A nonlinear autoregressive neural network with exogenous inputs (NARX) is trained using the acquired UGW signals and is utilized to evaluate rail track condition by extracting damage sensitive features (DSFs) based on the probabilistic density function (PDF) of the prediction error. First, a DSF baseline is obtained using the UGW data acquired from an intact rail track; then, for an unknown rail condition, the signals are processed by the trained NARX model to calculate DSFs; by comparing the calculated DSFs with the baseline, the rail condition can be evaluated. In this research, various UGW excitation frequencies are deployed, and for each frequency band an individual NARX model is trained. The prediction results show that the proposed method is highly sensitive to rail cracks, with an obvious increase in DSF values when an artificial crack is placed, which denotes the promising application of this method into SHM for mass rail transit systems.
Debond Localization in Honeycomb Core Sandwich Composites Using A0 Guided Wave Mode
Honeycomb core composite sandwich structures (HCCSSs) are widely employed in the marine, defense, and aerospace industries due to their lightweight design, the superior ratio of strength-to-weight and high load-bearing capabilities. Regardless of such commendable advantages, the skin-core junctions are susceptible to debond damage from intensive loading on these structures that may threaten the safety and structural integrity of the assembly. In this study, a novel framework for the guided wave based non-destructive evaluation (NDE) of a three-dimensional HCCSS is presented. The total focusing method with full matrix capture (TFM-FMC) technique is employed to detect and quantify debond at multiple locations in the finite element model of the three-dimensional HCCSS. The fundamental antisymmetric (A0) mode of guided waves is considered for multiple debond assessments in the investigated structure. The A-scan responses of the propagating guided wave of a debond HCCSS are compared with that of a pristine model. This comparison served as a valuable damage precursor resulting from interaction effects. A substantial amplification of the modal amplitude in the case of debonded HCCSS is noticed compared to that of the pristine model. The A-scan responses captured at a location distant from the debonded zone showed little difference. The TFM-FMC algorithm based structural monitoring framework is utilized to locate and quantify the debonded zones in the HCCSS. The proposed health monitoring framework is considered to be substantially effective in the detection of debond damages in a composite sandwich structure.
Lamb Wave Based Non Destructive Evaluation of Weld Quality in Thin Section Friction Stir Joints
Weld integrity assessment is critical for determining safety and dependability in the aerospace, civil, automotive, petrochemical as well as mechanical sectors. Non- Destructive Evaluation (NDE) based on Ultrasonic Guided Waves (UGW) is one of the finest emerging techniques for Structural Health Monitoring (SHM) systems, offering continual monitoring of similar, dissimilar, and composite materials. Traditional approaches need costly, time-consuming point-by-point examination, but Lamb wave based NDE has proven to be useful for wide-range, economical transmission, throughthickness, and in-situ inspection. The proposed study will conduct theoretical and experimental examinations of wave propagation through Al6061-Al5083 dissimilar Friction Stir Weld (FSW) of thin sections lap joints. In this investigation, two plates with thicknesses of 2mm each and dimensions of 250mm × 180mm are welded in a lap configuration with a fixed 30mm overlap length. Retro-reflective tape is attached at 30mm intervals on opposing sides of the stir zone. A Laser Doppler Vibrometer (LDV) is used to measure out-of-plane displacement of wave propagation. A high-voltage amplifier is utilized to generate 120V signals, along with that damping pastes are also used at sample boundaries to increase signal-to-noise ratios. Following NDE testing, weld samples were cross validated using SEM, Stereoscopy and CT scan enabling additional weld characterization. Using a single variable (at a time) method, both fundamental modes i.e., antisymmetric (A0) and symmetric (S0) were employed for weld characterization through process parameter optimization, which included tool rotational speed (RPM), transverse speed (mm/min), and corresponding sound weld window. Frequency wavelength (f-k) filtering techniques have been utilized for mode separation and further signal processing. The operative signal is a modulated sinusoid with central burst at Gaussian packet signal of 60, 80 and 100kHz, respectively. A preliminary investigation reveals the frequency response dependence of rotational and transverse speed in signal transmission. When rotational speed increases and transverse speed decreases, signal transmission enhanced up to a point due to improved material mixing capability, i.e., weld quality, but then declines due to excessive heat input, resulting in weld discontinuities such as tunnel defects, wormholes, voids etc. Our experiment discovered a direct correlation between Lamb wave scattering and defect size, which dictates weld quality; as we surpass the welding window, the possibility of defects escalates. The S0 mode conversions produce abrupt transmission responses at 80kHz for 1300RPM rotation speed, 75mm/min transverse speed and corresponding joint W2.
Wireless Gait and Respiration Monitoring Using Nanocomposite Sensors
The measurement of posture, loading conditions, and physiological parameters during physical activity is key in human performance monitoring and assessment. Of particular interest in this study is the monitoring of these parameters during high intensity activities associated with firefighting. The objective is to capture various walking cycles using nanocomposite pressure sensors built into the backpack-type harnesses of a Self-Contained Breathing Apparatus (SCBA) unit. It was hypothesized that the sensor’s high stability, linearity, and sensitivity would allow this sensor to be used for human posture and activity monitoring. First, nanocomposite sensors were prepared by integrating piezoresistive graphene nanosheet thin films between elastomer layers. Two sensors were then integrated with the SCBA harnesses and mounted at symmetric locations to capture gait and respiration cycles. Second, a small, portable, wireless data acquisition unit was developed to non-intrusively monitor up to eight sensors and wirelessly transmit the data for external data storage and analysis. This sensing node included analog signal conditioning circuits alongside an ARM based microprocessor for analog to digital conversion, signal processing, and wireless transmission. Third, human participant tests were performed while wearing these harnesses. The participants performed various low intensity walking and respiratory cycles. The results confirmed that the wireless sensing unit was able to reliably acquire sensor measurements, while the sensing streams also exhibited unique features indicative of different activities and postures.
Measuring the Golf Swing Pattern Using Motion Tape for Feedback and Fault Detection
Golfers must hone the sequence of the golf swing for consistent and efficient results, where a good golf shot is both precise and accurate. It takes countless hours of practice to develop the skill, and methods such as video feedback and coaching serve as aids to the process. However, few methods outside of visual observations exist to identify factors in the swing that cause a poor shot. Most golf analysis equipment is expensive and requires extensive setup time. Some systems only measure the physics of the ball and are limited to the practice range, whereas wearable sensors for golf are limited to specific motions such as the wrist. To address these challenges, a fabric-based, on-body sensor was developed and investigated to assess biomechanical movements during the swing. The wearable sensor, herein referred to as Motion Tape (MT), is a low-profile, disposable, self-adhesive, skin-strain sensor formed by spray-coating piezoresistive graphene nanocomposites directly onto kinesiology tape (K-tape). The objective of this study is to use MT to identify key movements in the swing sequence at four body locations: wrist, flexor carpi, anterior deltoid, and torso. First, MT sensors were fabricated for testing. Second, a human subject test protocol recording the golf swing of an experienced golfer was designed and conducted with participants wearing four MT sensors at the aforementioned locations. Last, the test data were processed, and the results showed that MT was able to identify unique movements during the swing. The MT data was analyzed using machine learning algorithms to identify movement abnormalities associated with poor swings. This allows for analysis of swing tempo for direct feedback to the golfer for improved performance.
Detecting Gait Abnormalities in Foot-Floor Contacts During Walking Through Footstep- Induced Structural Vibrations
This study aims to detect abnormal human gait patterns through the dynamic response of floor structures during foot-floor interactions. Gait abnormality detection is critical for the early discovery and progressive tracking of musculoskeletal and neurological disorders, such as Parkinson’s and Cerebral Palsy. Especially, analyzing the foot-floor contacts during walking provides important insights into gait patterns, such as contact area, contact force, and contact time, enabling gait abnormality detection through these measurements. Existing studies use various sensing devices to capture such information, including cameras, wearables, and force plates. However, the former two lack force-related information, making it difficult to identify the causes of gait health issues, while the latter has limited coverage of the walking path. In this study, we leverage footstep-induced structural vibrations to infer foot-floor contact profiles, which allows force-informed and more wide-ranged gait abnormality detection. The main challenge lies in modeling the complex force transfer mechanism between the foot and the floor surfaces, leading to difficulty in reconstructing the force and contact profile during foot-floor interaction using structural vibrations. To overcome the challenge, we first characterize the floor vibration for each contact type (e.g., heel, midfoot, and toe contact) to understand how contact forces and areas affect the induced floor vibration. Then, we leverage the time-frequency response spectrum resulting from those contacts to develop features that are representative of each contact type. Finally, gait abnormalities are detected by comparing the predicted foot-floor contact force and motion with the healthy gait. To evaluate our approach, we conducted a real-world walking experiment with 8 subjects. Our approach achieves 91.6% and 96.7% accuracy in predicting contact type and time, respectively, leading to 91.9% accuracy in detecting various types of gait abnormalities, including asymmetry, dragging, and midfoot/toe contacts.
Applying an Innovative User-Centric Co-Creation (UC3) Approach in Developing Intelligent Wearable Robots for Elderly Assistance: From a Transdisciplinary Lens
Mobility difficulties is a major public health issue which affects the ability of older adults to perform daily living activities and its prevalence increased with age. Assistive technology such as wearable robots has the potential to support older adults with mobility difficulties, yet there remain substantial challenges to developing wearable robots that can accommodate the needs of older adults and aid in performing daily living activities. In the current study, we developed and validated an innovative User-Centric Co-Creation (UC3) approach. First, we engaged older adults from the design phase of exoskeletons and treated them as equal partners. Second, we initiated an interactive testing platform to perform trilevel data curation, physiology, function, and behavioral level, to inform kinesiology-based parameters for wearable robots’ development. We invited a total of 16 older adults to join six co-creation workshops on wearable robots. Next, we used a multi-method pilot study using the UC3 approach to validate the interactive testing platform (N=15). After a successful pilot study and validation, a total of 157 participants were recruited in two waves. First, we recruited 91 healthy older adults aged 65 or above, between July and August 2021 to act as the reference group. Second, we invited 66 older adults with mobility difficulties between December 2021 and December 2022, who are the target users of the wearable robots. Subsequently, a total of 55 participants in the second wave joined an experiment with knee robots between May 2022 and February 2023. All the participants were invited to join experiment procedures at three levels: physiology level, function level, and behavior level. Gait motion analysis and balance ability were included at the physiology level. Maximum voluntary contraction at three knee angles (performed using knee extension test) and maximum handgrip strength were included at the function level. The Short Physical Performance Battery, a group of measures that combines the results of 4-meter walk speed, 5-time chair stand test, and balance tests, was included at the behavioral level. Following the UC3 approach, we engaged older adults as equal partners in wearable robots’ development and developed a performance-based risk hierarchy with a transdisciplinary team’s support. Prior to conducting the three-level analysis to inform the development of wearable robots, we instigated a risk hierarchy based on recommended cut-offs on handgrip strength (M: < 28 kg, F: < 18 kg), 4-metre walk speed (< 1.0 m/s), 5-time chair stand test (≥ 12 s), and SPPB total score (≤ 9). Among all the 157 participants, 29 (18.5%) were classified as having no risks, 51 (32.5%) were classified as having one risk, 29 (18.5%) were classified as having two risks, 20 (12.7%) were classified as having three risks, the remaining 28 (17.8%) were classified as having four risks. In general, we found evidence for a novel UC3 approach to inform wearable robots’ development. We started with a full engagement of target users, followed by a trilevel data curation at the physiology level, function level, and behavioral level. Lastly, continuous improvement and discussions with experts in a transdisciplinary team confirmed the validity of the UC3 approach. All in all, elucidating the unmet needs for daily activities at the physiology, function, and behavioral level will provide valuable insights into the development of intelligent wearable robots and will unlock the key to an independent living lifestyle in old age.
Improving Human Balance with Wearable Devices
This study investigates the use of an optical-based balance sensor and a musclemimetic wearable robot to evaluate and improve balance in elderly people with various health conditions. We analyzed data from 149 subjects to extract critical temporal and frequential features related to balance, such as center of pressure (CoP), center of gravity (CoG), and theta angles, to categorize them into five distinct levels (i.e., levels 0,1,2,3, and 4). A correlation analysis between balance sensor features and gastrocnemius lateralis (GL) muscle maximum voluntary contraction (MVC) confirmed our hypothesis that subjects with stronger GL muscles maintained better balance. Hence, we developed a wearable muscle-mimetic robot to compensate for the weakened GL muscle. The GL muscle-mimetic ankle robot, which mimics muscle mechanical properties and uses human intrinsic physiological signals for control, simultaneously relaxes and contracts with the GL muscle to enhance postural stability and balance. Furthermore, testing the wearable robot with the balance sensor demonstrated promising results in enhancing balance, as evidenced by the decreased variance of CoP when the robot was worn. Our findings suggest that the optical-based balance sensor and muscle-mimetic wearable robot provide an effective approach for assessing and improving balance in elderly individuals, with potential implications for reducing fall risk and improving postural stability in aging populations.
Emotion Recognition Using Footstep-Induced Floor Vibration Signals
Structural vibrations induced by human footsteps contain rich information that can be used for a wide range of applications, including occupant identification, localization, activity recognition, and health and emotional state estimation. Among these, emotion recognition holds great potential for improving smart buildings by enabling mental health monitoring and human-centric services. Existing emotion recognition approaches use cameras, wearables, and mobile devices to capture people’s changing gait patterns under various emotional states. However, these approaches come with corresponding drawbacks, such as being limited by visual obstructions and requiring users to carry devices that cause discomfort. To overcome these drawbacks, we introduce a new emotion recognition approach using footstep-induced structural vibration signals. The main intuition of this approach is that people’s gait patterns change under various emotions [1], thus inducing distinct structural vibration patterns as they walk. Compared to other methods, our approach is non-intrusive, insensitive to visual obstructions, and has fewer perceived-privacy concerns. The main research challenge in developing our approach is that emotions have both explicit and implicit effects on gait, making the explicit gait parameters insufficient to describe such a complex relationship. To this end, we develop a set of emotionsensitive features from the vibration signals, including gait parameters, sequential features, and time-frequency spectrum features to capture both the explicit and implicit effects of emotion on gait. To better integrate multiple types of features, we develop the fully-connected layer, the long-short-term-memory (LSTM) layer, and the convolutional layer to extract information from the features and a multilayer perceptron to estimate emotion. Our approach is evaluated in a real-world walking experiment involving 5 participants with over 100 minutes of footstep-induced floor vibration signals. Our results show that our approach achieves a mean absolute error of 1.33 for valence score estimation and 1.26 for arousal score estimation out of an overall score range of 1 to 9, which has an accuracy of 72% for High / Low valence classification and 82% for High / Low arousal for emotion classification.
Smart Sensing Technology and its Aid in Flood Data Analysis for Northern New Mexico
This paper addresses the need for infrastructure protection in Ohkay Owingeh, a tribal community located in a high desert region with a pronounced monsoon season. The extended dry period of 8-9 months makes the area susceptible to flooding during the monsoon season, leading to significant disruptions in transportation, infrastructure damage, and the displacement of tribal members. To mitigate these challenges, the adoption of smart sensing sonar LEWIS technology is proposed. The LEWIS sonar system will enable the detection of flood activity by measuring water level fluctuations. This valuable information will provide tribal members with an alert system to monitor and respond to flood events promptly. Moreover, the data gathered by the LEWIS Sonar will empower the tribal community of Ohkay Owingeh to take control of the current situation and make informed decisions for future flood prevention measures.
Human-Machine Interfaces Using Augmented Reality
In recent decades, infrastructure aging, new social behavior, urban and rural environmental changes, and nature extreme events have increased in complexity. When disasters occur, saving lives, and providing access to emergency teams is a priority where every second saved is critical. Researchers in Structural Health Monitoring (SHM), government laboratories, and industry leaders are using sensors, field deployments, algorithms, and signal processing to assist and prioritize decisions. If the collection of data would be accelerated with near-real time interface with engineers in the field, emergency responders, safety, and maintenance could be integrated with machines in real-time, and new solutions could be advanced. This paper summarizes new work on human decisions exploring the concept of humanmachine- structure interfaces associated with structural dynamics and damage, to transform human decisions using new interfaces. The interface between human and structures is achieved with Augmented Reality (AR). The results include work in human-in-the-loop with application on near real-time computer vision, human-robot teaming, and a new infrastructure maintenance paradigm centered in augmenting the capabilities of humans in the field. The platform of human-robot teaming is further advanced in dynamics and control of humans using data obtained from robots and vice versa. Future research includes human-centered inspections, and humanmachine control theory.
Energy Based Ultrasonic Techniques for Earlystage Damage Detection in Concrete Structures
During the service life, concrete structures undergo several adversities that lead to weakening in concrete and results in crack initiation. At initial stage of damage, breathing /clapping effect prevails inside the material. It results in additional nonlinearity, called as contact acoustic nonlinearity (CAN) in the structure which is often subsumed due to the material nonlinearity and leads to unidentified progression of damage inside structure. In the present study, ultrasonic investigations have been carried out to detect the flexural cracks in a scaled model of a reinforced concrete (RC) girder-deck system under four-point bending where the damage stages can be categorized as, undamaged (Stage 0), crack initiation (Stage I), crack propagation (Stage II), and severe damage (Stage III). Data acquired from ultrasonic actuation-sensing at different damage stages are processed in time-frequency domain and frequency domain using energy based ultrasonic parameters, such as, wavelet packet energy and spectral energy distribution. Further, a new energy based nonlinear acoustic parameter (ENAP) is introduced that works on the harmonic phenomenon induced by the breathing cracks. The proposed energy based methods (especially nonlinear methods) are found to be very efficient for detecting all the damage levels successfully.
Quasi-static Component Generation of Guided Waves and Its Applications on Damage Detection
Quasi-static component (QSC) generation is a typical nonlinear effect of ultrasonic wave propagation, which can be used for materials characterization with the features of high sensitivity and low attenuative effect (generated QSC pulse's carrier wave frequency is zero). This study presents a novel method for early-stage microdamage detection and localization using QSC generation of ultrasonic guide wave L(0,1) mode propagation in pipes under group velocity mismatching condition. Finite element (FE) model was developed to provide insights on the generation and propagation characteristics of the QSC induced by both the global weak material nonlinearity and local microdamage in metallic pipes. FE analysis verified the L(0,1) mode of the QSC. The cumulation feature of QSC pulse was obtained in terms of the temporal pulse width and the pulse amplitude. A microdamage localization method was pro-posed based on the L(0,1)-QSC mode pair. Experiments were conducted in corrosion-induced aluminum pipes to investigate the QSC pulse signals. The QSCrelated nonlinear pulse signals caused by the material nonlinearity and micro-damage were detected and identified. The mode of QSC and feasibility of the proposed microdamage localization technique were verified. The FE and experimental results demonstrated the basic properties of QSC generation in isotropic metallic pipes and validated potential applications.
Autonomous Robotic Inspection based on Active Vision and Deep Reinforcement Learning
Over the years, various methods based on computer vision have been proposed for the problem of damage detection and segmentation. Recently, remarkable progress has been made in this field owing to the emergence of deep neural networks. However, there is a main assumption among these works that the data collection (e.g., taking photos) is usually carried out by human inspectors, so there is little occlusion or bad lighting conditions. With that being said, the uncertainties that could occur in data collection are handled manually to ensure the dataset is clean and free of confusing or occluded damages. This assumption limits the applicability of autonomous robotic inspection to real-world settings due to the uncertainties in data collection and data interpretation. To bridge this gap, this study integrates the concept of active perception into damage detection and proposes a framework based on the Partially Observable Markov Decision Process (POMDP) and Deep Reinforcement Learning (DRL). The proposed framework facilitates the learning process for robotic agents to explore the 3D environment and intelligently select informative viewpoints to reduce uncertainty and minimize confusion, which leads to more reliable decision-making. Besides uncertainty reduction, the DRL agent can also inspect the workspace more efficiently compared with traditional raster scanning. The trained DRL agent is evaluated for the autonomous assessment of cracks on metallic surfaces. Results show that the agent equipped with the active perception module outperforms the raster scanning inspection by 57% in terms of crack IoU. In addition, the DRL agent can reduce the total inspection time by two times while the prediction accuracy is on par with the raster scanning.
Robotic Ultrasonic Inspection of Large and Complex Structural Assets
The implementation of robotic inspection solutions can provide massive improvements in the non-destructive testing and evaluation of large industrial structural assets. Current on-the-market remote inspection solutions are often limited in scope and lack the positional accuracy necessary for robust and repeatable structural health monitoring. This paper, therefore, describes multiple robotic navigation and inspection techniques to tackle a variety of challenges. Conventional inspection systems utilizing ultrasonic phased arrays for defect detection in large structures like storage tanks and flat steel plates typically employ a localized inspection approach. However, these systems often suffer from slow inspection speeds and an increased potential for human error, particularly in the case of large structures requiring repetitive inspections. The first challenge addressed is the inspection of large, thick steel plates used in the manufacture of industrial assets. The proposed solution focuses on achieving precise positional accuracy for spot gauge-thickness measurements and detecting laminations and inclusions. The system's novelty lies in utilizing the inspection sample itself for positional tracking. While the introduction of an automatic crawler-based approach speeds up the inspection process, it remains time-consuming and generates a substantial amount of data. To tackle this challenge, when the thickness of the inspected component allows, ultrasonic guided waves (UGW) can be employed for mid to long-range inspections. The second example outlines the integration of UGW with robotic inspection systems, which could significantly reduce inspection times by enabling rapid scanning and providing access to inaccessible regions. The final system looks at complex geometry, such as welded inserts or piping networks, and the implementation of ultrasonic phased array capabilities remotely and accurately. Standard crawlers for weld inspection are limited to simple geometry such as plates and large-bore pipes. The proposed system combines the flexibility of a sixdegree- of-freedom robotic manipulator with the remote capabilities of a magnetic base. Imbedding a sensor-driven system for live geometry assessment and tool path planning.
Reinforcement Learning-based Bridge Inspection Management
Biannual inspections are required to assess the physical and functional condition of our nation’s bridges. The Federal Highway Administration (FHWA) and various Departments of Transportation (DOT) in the United States periodically update specifications and techniques to normalize and advance the bridge inspection procedures. However, some ambiguity remains in these inspection requirements. One relevant example relates to inspection intervals and techniques. Currently, FHWA requires routine bridge inspection at least every two years, and if necessary, inspectors can adjust the inspection frequency. The details of how one would adjust the inspection frequency is not specified. And while many advanced techniques, e.g., ultrasonic surface wave and AI-based image inspection methods, can be applied to inspect bridges, the rationale to use these techniques relies on bridge inspectors’ experience. This study focuses on developing a reinforcement learning-based method to assist inspectors in managing bridge inspection planning. In this method, a reinforcement learning algorithm is utilized to optimize the frequency of inspection and the selection of the inspection method. A physics-based damage development model is utilized to simulate the deterioration process of the bridge. The reward function designed in the reinforcement learning process considers both economic cost and inspection plan risk. After training, the reinforcement learning agent can rapidly determine an optimal bridge inspection policy based on a bridge’s state, which can minimize both the cost and the risk of bridge inspection work. Thus, inspectors can refer to this agent to make a specific inspection plan for each bridge based on a bridge’s design, history, and features.
A Streamlining Remote Sensing and Digitalization Process for Bridge Inspection
One of the major challenges that prohibited the wide application of remote sensing photogrammetry in modern bridge inspection is the lack of advanced sensing platforms with a data analysis process. This study explores remote sensing for bridge inspection with a suite of unmanned aerial system (UAS) platforms accompanied by a follow-up digitization process for bridge inspection. To fully inspect a bridge of interest, a team of four complementary remote sensing UASs and equipment is employed, including a confined-space-specific Elios2 drone for bridge underdeck inspection, a DJI M600 equipped with LiDAR, thermal and hyperspectral sensors for above bridge deck sensing, an Anafi Parrot for side and above bridge deck RGB imaging, and a supplementary hand-held high-resolution camera for drone-inaccessible and highpriority areas. The digitized bridge inspection results cover multiple aspects of the bridge, including an interactive digital object represented by a dense point cloud, RGB images to reflect any visible surface deterioration (such as cracks, spalling, corrosion, and efflorescence), thermal images to reflect bridge deck near-subsurface delamination, and hyperspectral images to reflect any invisible bridge surface change. The digitized bridge model provides a comprehensive but intuitive digital representation of the inspected bridge. Compared to traditional bridge inspection practices with descriptive and subjective results, remote sensing photogrammetry shows great potential in efficient and consistent bridge inspection with fine details, which is believed to be necessary for reliable bridge condition assessment and long-term bridge health monitoring.
DamBotTM: An Unmanned Amphibious Vehicle for Earth Dam Outlet Inspection
The US Army Corps of Engineers (USACE) owns, maintains and operates several hundred locks and dams across the United States. A large portion of these structures have met or exceeded their design life, therefore the need to perform detailed inspections regularly has become increasingly important. Some of the challenges for inspection personnel are the hazardous conditions associated with entering dam outlet works, and the need to conduct the inspection within a short time frame so that the dam can resume normal operations and maintain downstream water levels. These subterranean conduits are classified as confined spaces and can be several hundred meters long, have flowing water, and in some instances have toxic gases present. The USACE Engineer Research and Development Center (ERDC) has developed an unmanned amphibious vehicle, called DamBot™, to enter these outlet works with a sensor suite and perform first-look inspections of the conduit and closure gates. In order to carry out these inspections, DamBot™ is equipped with cameras and LiDARs to capture 360 degrees situational awareness around the platform, and a five-meter robotic arm with nine degrees of freedom to perform up close inspection of closure gates, which can be over six meters tall. Additionally, the DamBot™ is able to capture data of the entire conduit during inspections, collecting a dataset that provides a comprehensive picture of the infrastructure. DamBot™ uses techniques such as simultaneous localization and mapping (SLAM) to assure positional accuracy of data collection in these GPS denied environments. These datasets can be post-processed into 3D models and can be used for structural health monitoring by way of change detection when compared with previous inspections. DamBot™ has been successfully demonstrated at several active USACE projects, and this paper will detail the specifications of the system and discuss the results of field demonstrations, lessons learned, and future improvements to the system.
Deep Learning-Based Corroded Crack Identification in Reinforced Concrete Using SCNet Model
In order to improve the efficiency and accuracy of corroded cracks detection and classification in reinforced concrete, a corroded cracks identification model Steel Corrosion Net (SCNet), based on deep learning Convolutional Neural Network (CNN), is proposed. The SCNet combines massive initial data with a multi hidden layer neural network framework, and achieves feature learning and accurate classification through model training. The data set of 39000 crack figures is firstly built by original data collection and data enhancement. The training process of the SCNet consists of defining the loss function, the selecting back propagation optimization algorithm, continuously entering data to the network framework and running back the propagation algorithm until the error drops to a certain range. Afterwards, a SCNet three-classification neural network model is built and tested using TensorFlow learning framework and Python. According to the training and testing accuracies of the model, the structure and parameters of the SCNet network are optimized. The results of SCNet are compared with those obtained by two traditional testing methods. The results show that the proposed SCNet model achieves a classification accuracy of 96.8%. In other words, it can effectively identify and classify the corroded cracks in reinforced concrete, with high accuracy and measurability.
Structural Health Monitoring with Robot and Augmented Reality Teams
Mobile robots can access regions and collect data in structural locations not easily reached by humans. This includes confined spaces, such as inside walls, and underground pipes; and remote spaces, such as the underside of bridge decks. Robot access provides the opportunity to sense in these difficult to access spaces with robot mounted sensors, i.e. cameras and lidars, and with the robot placing and servicing standalone sensors. Teams of robots, sensors and AR-equipped humans have the potential to provide rapid and more comprehensive structural assessments. This paper presents results of studies using small robots to explore and collect structural condition data from remote and confined spaces including in walls, culverts, and bridge deck undersides. The presentation also covers system and network architecture, methods for automating data processing with localized and edge-based processors, the use of augmented reality (AR) human interfaces and discusses key technical challenges and possible solutions.
Augmented Reality with Live Video Streaming for Beyond Visual Line of Sight Inspection for a Steel Bridge
A Bridge inspection is routinely conducted to enhance its useful life and ensure user safety within prescribed standards. In this paper, an automated inspection of steel bridges beyond the visual line of sight (BVLOS) is explored with the use of a climbing robot providing live video streams in an augmented reality (AR) environment. We anticipate a reduction in computational complexity involved in stabilizing video feeds due to camera jitter and rolling shutter effects, improved image resolution of observed defects hence their early detection, and reduced training effort to operators through our intuitive interface. Previously, AR has been employed in bridge inspection to visualize building information modelling (BIM), evaluate impact echo delamination, observe bridge deterioration information, update BIM data with higher precision, reduce ambiguity in data collection, support in-office preparations, and extract 3D measurements, using ground robots in collaborative systems. To the best of our knowledge no previous attempt has been made to investigate a collaborative bridge inspection system aided by AR for teleoperation of climbing robots on steel bridges in BVLOS scenarios. The outcome of this research is anticipated to guide future bridge inspection standards towards more computationally efficient systems, minimizing the cognitive workload of the robot pilots compared to unmanned aerial vehicles (UAV) pilots, improving cost effectiveness, with minimal disruption to traffic. Use of real-time audio-visual augmentations will be initially evaluated for a two-person human-robot collaborative system. Visual feedback will be employed to teleoperate the robot on a lab-based steel bridge model while audio feedback is employed to coordinate the two operators across a wireless communication network.
Monitoring of Freezing Water or Melting Ice in Aircraft Fuel Tanks and Fuselages by Acoustic Emission
It is a mostly overlooked phenomenon in physical chemistry that matter produces characteristic sound emissions when passing through a phase transition. The origin of this sound is not yet fully understood, but it is certain that sudden changes in volume, cracks, friction between crystallites and other sources must be considered, but a generally accepted theory of the "sound of phase transitions" is lacking. But even if the cause is not completely clear, this phenomenon can be used to detect the presence of these substances in technical structures. This is important, for example, in the case of water and ice in aircraft fuel tanks. Water arising from contamination and condensation is frequently found in fuel tanks and regular drainage procedure are important to guarantee the safety of systems and structures. While ice dispersed in the kerosene phase can seriously hinder the combustion process, blocks of ice can cause serious crack formation as in valves and tubes. A practical problem with all drainage procedures after flights at freezing conditions is the right time to start that process. Starting too early would mean that the remaining ice cannot be removed and waiting too long is an economic problem because it increases the aircraft's downtime. Unfortunately, there is no technology yet that tells mechanics when all the water has melted. It would be beneficial for maintenance teams to find the right time to drain, and acoustic emissions from the melting ice would be the ideal tool to determine this moment. For this purpose, acoustic sensors are attached to the skin of the tank walls, and the acoustic signals from the ice are intense enough to propagate through media, the aluminum sheets and coating. In this way, the completion of the ice melt is determined by the time at which the acoustic emission stops. We present measurements on a laboratory scale, results from a realistic climate chamber on a tank model and the first results from a campaign on an operational aircraft (Airbus A330). We were able to show that relevant amounts of melted ice can be reliably determined, e.g., 30 ml of water under the kerosene phase in a replica fuel tank generate about 200,000 distinguishable signals, in our case these signals are acoustic transients. This is quite sufficient to obtain the desired information about the state of the melting ice. In addition, to address other structures, results of ice melting and water freezing processes on an aircraft fuselage model are presented.
Vibration Monitoring of Hydropower Systems
Excessive vibration in hydropower systems is often an indication of a failure of equipment that can lead to fatigue issues or complete shutdown of a system. Localizing the source of the vibration and identifying the cause is crucial to evaluating the structural integrity and ensuring its continued operation. At a hydropower plant in Arkansas, substantial vibration was being generated within one of the hydropower units when brought offline, when no flow should be occurring through the unit. On site personnel suspected that the vibrations were coming from the wicket gates, whose opening and closing controlled the flow of water through the unit. Observations lead to the belief that leakage-flow induced vibrations were being generated by a permanent gap between adjacent gates in the closed state. Within the hydropower unit, the wicket gates are fully submerged underwater, and visual inspection of the body of each gate requires the unit to be completely drained of water. Thus, a short-term monitoring program was designed to use accelerometers to measure the magnitude of vibration coming from the wicket gates using only the gate stems accessible along the outer ring of the unit. The source of the vibration was localized by investigating the energy of vibration produced from each wicket gate and where the vibration initiated as the gates transitioned from open to closed. The results of this study were verified during a later dewatering of the unit where gaps between adjacent gate pairs were measured. The results of this effort provide a method for localizing excessive vibration in hydropower units in need of maintenance.
“Gen III” Piezoelectric PMN-PZT Single Crystal Sensors and Actuators for Structural Health Monitoring Application
Crystallographically engineered Relaxor-PT single crystals, specifically PMN-PT (Generation I) and PIN-PMN-PT/PMN-PZT (Generation II), offer much higher piezoelectric and electromechanical coupling coefficients (d33 > 1,500 pC/N, k33 > 0.9), when compared to polycrystalline PZT ceramics. Recently Ceracomp Co., Ltd. (www.ceracomp.com) has developed the solid-state single crystal growth (SSCG) technique and successfully fabricated Gen III PMN-PZT single crystals modified with acceptors or donors [Fig. 1]. The piezoelectric constants (d33) of (001) Gen III PMN-PZT single crystals were measured to be higher than 4,000 pC/N and thus about two times higher than those of PMN-PT/PZN-PT (Gen I) and PIN-PMN-PT/PMN-PZT (Gen II) single crystals. The Gen III PMN-PZT single crystals have been firstly applied to single crystal-epoxy composites, ultrasonic transducers, piezoelectric sensors, and piezoelectric actuators [Fig. 2]. In this presentation, we will introduce the development of high performance piezoelectric sensors and actuators by using the Gen III PMN-PZT single crystals for SHM (structural health monitoring) application.
Curing Monitoring of Composite Patch Using Electromechanical Impedance
The repair technology of surface-bonded composite patches has garnered significant attention in the engineering field. The quality of the repair is directly impacted by the curing degree of the patch. Thus, monitoring the curing process of the patches is necessary. This paper proposes a real-time monitoring method for the curing of composite patches based on electromechanical impedance (EMI) technology. The proposed method can accurately track the curing process of composite patches. A piezoelectric sensor was embedded in T300 woven carbon fiber prepreg, and EMI signals were collected at 2-minute intervals during the curing process. Dielectric analysis (DEA) was performed to verify the effectiveness and feasibility of the method. The resonant resistance and correlation coefficient (CC) index were utilized to characterize the curing process of the patches. The experimental results demonstrate that the resonant resistance remains stable when the resin is converted to approximately 85%. The development trend of the CC aligns with the curing behavior of the resin, and the rate of change of the CC can reflect the speed of resin conversion. Furthermore, the proposed method can accurately monitor the curing process of both the carbon fiber prepreg patches and those bonded to an aluminum plate, demonstrating the universality of this method.
A Few-Shot Learning Framework for Rotor Unbalance and Shaft Crack Fault Diagnostic Based on Physics-Informed Neural Network
This study aims to detect and localize rotor unbalance and shaft crack damage in a few-shot data scenario. It proposes a reinforcement learning-based approach with physics preferences (called RLP2). RLP2 is used to guide the physics consistency in the rotor finite element mimetic neural network (RFEMNN). The RFEMNN is first trained in an unsupervised manner using mixed simulation and experimental datasets in the task of reconstructing rotor’s vibration signals. Then, the RFEMNN is fine-tuned in the RLP2 framework using a physics preference reward as policy loss to ensure similarity between hidden layer output and rotor system parameters. The output of the RFEMNN is fed into a downstream multi-output convolutional neural network (CNN) for fault diagnostic and localization. The proposed method’s effectiveness is demonstrated through experiments on a PT500 platform under zero-shot, one-shot, and few-shot learning scenarios. The obtained results indicate the potential of this method for the predictive maintenance of rotor systems in real-world applications with limited training data.
Evaluation of U-Net for Time-Domain Full Waveform Inversion Improvement
Ultrasound computed tomography (USCT) is one of the advanced imaging techniques used in structural health monitoring (SHM) and medical imaging due to its relatively low-cost, rapid data acquisition process. The time-domain full waveform inversion (TDFWI) method, an iterative inversion approach, has shown great promise in USCT. However, such an iterative process can be very time-consuming and computationally expensive but can be greatly accelerated by integrating an AI-based approach (e.g., convolution neural network (CNN)). Once trained, the CNN model takes low-iteration TDFWI images as input and instantaneously predicts material property distribution within the scanned region. Nevertheless, the quality of the reconstruction with the current CNN degrades with the increased complexity of material distributions. Another challenge is the availability of enough experimental data and, in some cases, even synthetic surrogate data. To alleviate these issues, this paper details a systematic study of the enhancement effect of a 2D CNN (U-Net) by improving the quality with limited training data. To achieve this, different augmentation schemes (flipping and mixing existing data) were implemented to increase the amount and complexity of the training datasets without generating a substantial number of samples. The objective was to evaluate the enhancement effect of these augmentation techniques on the performance of the U-Net model at FWI iterations. A thousand numerically built samples with acoustic material properties are used to construct multiple datasets from different FWI iterations. A parallelized, high-performance computing (HPC) based framework has been created to rapidly generate the training data. The prediction results were compared against the ground truth images using standard matrices, such as the structural similarity index measure (SSIM) and average mean square error (MSE). The results show that the increased number of samples from augmentations improves shape imaging of the complex regions even with a low iteration FWI training data.
Physics-Informed Gaussian Processes for Wave Loading Prediction
The quantification of wave loading is an important step within the estimation of fatigue accrual within offshore structures. The direct measurement of wave loads can be both challenging and expensive, often requiring the installation of bespoke systems if at all possible. The estimation of wave loads based on data from other sensors, more commonly found on offshore structures (e.g. wave radars) is therefore highly desirable. This paper presents an experimental study of a monopile structure within a wave tank, instrumented with accelerometers, strain gauges, a force collar, wave gauges and a velocimeter subject to a range of wave conditions. Here the dataset is used to construct models which combine data-based Gaussian process NARX models with linear wave theory. The novel model structures presented rely on only wave gauge data as an input and achieve improved predictive performance over purely data-based approaches across a range of wave states.
Fast Prediction of Dynamic Structural Response Using Reduced Basis Function Combined with Neural Network
Frequent calibration of the dynamic characteristics of jack-up platforms are important for accurate evaluation of their safety and comfort to occupants. As jack-ups are large in scale involving many physical mechanisms and hydrodynamic coupling, it is challenging to rapidly optimize the estimation of system parameters and predict the dynamic responses under potential scenarios. This study develops a novel offline-online framework to address these issues. In the offline phase, a set of reduced basis functions is extracted from a collection of high-fidelity datasets, and the corresponding coefficients are employed to train neural network models. The online phase involves mapping model parameters to the coefficients of reduced basis functions using the trained neural network. The trained neural network combined with the reduced basis function is then utilized to predict dynamic response. The feasibility of the proposed method was evaluated through a numerical model of a jack-up under wave loads. The results indicate that the method is effective, robust, and promising for the rapid evaluation of large-scale structures.
From Initial to Final State: Single Step Prediction of Structural Dynamic Response
Accurately predicting the structural response under dynamic loads is of great importance to evaluate the structure's performance, monitor the structure's health and control the vibration. The development of a real-time prediction method is challenging because the model must have a low computational burden and high running speed. Due to the computing convergence requirements and complexity constraints, traditional numerical methods such as the finite element method and the finite difference method must take thousands to millions of short-time steps to calculate the final structural dynamic response we really want. After costing a lot of computing resources and time, these internal steps are worthless but significantly reduced the efficiency of numerical methods. To tackle this problem, in this paper, we propose a single-step method named physics-informed implicit Runge-Kutta (PI-IRK) to predict the structure dynamic response straightly from the initial to the final state. Specifically, we fuse discrete-time physics-informed neural networks (PINNs) and implicit Runge–Kutta method with low-expense hide stages. In the proposed method, deep neural network models are employed as the core to predict the Runge-Kutta stages and the final state. We integrate physics information such as implicit Runge–Kutta form of structure vibration governing equation and boundary constraints as the prior information into the neural networks model. With the assistance of the prior information, the proposed PI-IRK model is an unsupervised learning model that can be trained without any measurement data. Without any internal steps, the PI-IRK model can straightly predict the final structural dynamic response after training. The accuracy of the proposed method is demonstrated by predicting the structural response of a cantilever beam under a distributed dynamic load even with a large time step.
Physics-Informed Transfer Learning in PBSHM: A Case Study on Experimental Helicopter Blades
Data for training Structural Health Monitoring (SHM) systems are often expensive or infeasible to obtain. Population-based SHM, which considers data across a population of structures, presents a potential solution to this issue. However, as differences between structures can lead to differing training and testing distributions, conventional machine learning methods may not generalise between structures. To address this issue, transfer learning (TL) can be used to leverage information across related domains. An important consideration when applying TL is how to asses similarity to identify and extract shared information. In unsupervised TL, a major challenge is that previous data-based metrics are limited to quantifying marginal distribution similarity in the unsupervised setting. This paper proposes utilising the Modal Assurance Criterion (MAC) between modes of healthy structures as a measure of data similarity to identify features that minimise conditional distribution shift. The MAC is incorporated into a feature selection criterion and a TL methodology is proposed. Moreover, the proposed methodology is shown to facilitate label sharing within a heterogeneous population of helicopter blades
Partially Structured Gaussian Processes for Grey-Box Learning in SHM
Combining physics with data-driven learning seeks to harness the benefits of the expressivity of machine learners whilst having known physical laws that the model must adhere to. Building these types of models have the advantage of being able to represent phenomena more complex than can be described with simplified analytical equations, whilst simultaneously allowing for greater prediction confidence and interpretability, as well as providing a mechanistic basis that predictions can revert to when data becomes scarce. With operators more frequently carrying out monitoring campaigns, and decades of engineering knowledge to call upon, it is a natural step for health monitoring practitioners to adopt physics-informed learning strategies for monitoring structures. In many engineering scenarios, it is often the case that one will not have complete physical knowledge of a structure or system of interest, but partial knowledge, for which it is desirable to encode in a machine learning model. In this paper, we will explore how this type of knowledge can be embedded into Gaussian process models for processes that naturally exist as products of constituent functions, such as for processes that have both spatial and temporal dependencies. Focus will be placed on how the properties of the covariance function that has embedded physical structure dictates the predictive regime that the model may operate in within the partial knowledge setting. Application of the developed models includes learning the decoupled response of a beam under random loading into the constituent mode shapes and temporal response.
Physics-Informed Neural Networks for One- Step-Ahead Prediction of Dynamical Systems
During online implementation of vibration-based structural health monitoring (SHM) strategies, forward prediction of the system state may allow for improved detection speed. With adequately fast forward prediction, feedback systems can also be improved to provide in-time control plants. When using solely physics-driven models, small discrepancies between the physical system and digital model can result in significant deviation between the estimated and true output. On the opposite end, a solely data-driven model can only reasonably be applied in an identical, or sufficiently similar, scenario to that for which the data was collected. Using machine learning to combine data with known physics is a well-proved approach to overcoming this issue. One such method for this approach is the use of physics-informed neural networks (PINNs), which can be implemented as either a forward modeller, or a constrained learner, for equation solution discovery, or equation discovery. The former of these aims to provide the desired output from the governing physical equations, whereas the latter estimates the parameters in these equations. A significant advantage of PINNs is, given a suitable network architecture, the high speed and low computational-cost of their prediction step, which positions them as a useful approach for real-time estimation, given adequate training. A common assumption for PINNs is that the embedded physics is exhaustive with respect to the ‘true’ model. In this paper, a novel PINN-based architecture is presented to rapidly forward-predict the state of a dynamic system given an initial state. For the state estimation, the PINN acts as an equation solution discovery approach, and the novelty of the architecture here is to provide a more generalised predictor which can be applied to a wider range of instances. The PINN is intended to deliver its prediction within a prescribed time frame, which equals the sampling time of the acquisition/control system, and is here assessed against this goal.
Physics-Informed Guided Wavefield Data Completion
Ultrasonic wavefields are widely employed in nondestructive testing and structural health monitoring to detect and evaluate structural damage. However, measuring wavefields continuously throughout space poses challenges and can be costly. To address this, we propose a novel approach that combines the wave equation with computer vision algorithms to visualize wavefields. Our algorithm incorporates the wave equation, which encapsulates our knowledge of wave propagation, to infer the wavefields in regions where direct measurement is not feasible. Specifically, we focus on reconstructing wavefields from partial measurements, where the wavefield data from large continuous regions are missing. The algorithm is tested on experimental data demonstrating its effectiveness in reconstructing the wavefields at unmeasured regions. This also benefits in reducing the need for expensive equipment and enhancing the accuracy of structural health monitoring at a lower cost. The results highlight the potential of our approach to advance ultrasonic wavefield imaging capabilities and open new avenues for Nondestructive testing and structural health monitoring.
Damage Identification for Plate Structures Using Transfer Learning Physics-Informed Neural Networks
Recently, there has been a growing interest in the development of intelligent labelfree structural damage identification methods that utilize physics-informed neural networks (PINNs). However, penalizing the governing equation of training data is computationally time-consuming since the existence of high-order partial derivatives. To address this issue, a damage identification method for isotropic and homogeneous thin plates is proposed in this paper that utilizes transfer learning physics-informed neural networks (TL-PINNs). TL-PINNs are efficient PINNs that solve inverse problems by leveraging transfer learning. Transfer learning is a machine learning technique that leverages knowledge from a source task to enhance performance on a related but different target task. It involves reusing a source model trained on a source task and then fine-tuning it to a target model with a target task. In the proposed method, the source model is trained to minimize the mismatch between training data and its predictions. Then, it is finetuned as the target model by minimizing both the mismatch between training data and its predictions as well as residuals that penalize the governing equation of isotropic and homogeneous thin plates. It is resulting in fewer iterations being required in training to penalize the governing equation than those in PINNs, which is time-consuming for highorder partial derivatives using automatic differentiation. Hence, TL-PINNs have a substantial reduction in computational time compared to PINNs for damage identification. A trained TL-PINN from a measured flexural guided wavefield is referred to as a pseudopristine model since it can generate a wavefield that approximates that governed by an isotropic and homogeneous thin plate. This unique functionality arises from penalizing the governing equation in the target model and the fact that the governing equation does not consider the existence of the damage. Any local anomalies in the measured wavefield can be isolated by comparing them with the wavefield generated by the pseudo-pristine model and then intensified using the Teager energy operator. An accumulative damage index is formulated, and the damage can be identified within neighborhoods with high index values. The effectiveness of the proposed method is demonstrated through a numerical investigation. A parameter study is also conducted to investigate the robustness of TL-PINNs with different hyper-parameters.
Temperature-Responsive and Passive Wireless Mechanical Metamaterial Sensors
Knowing the location and environmental conditions of sailors that have fallen overboard can greatly facilitate open water search and rescue operations. Currently, active sensors that trigger an alarm when a sailor falls overboard exist, and it works by detecting a loss of signal from a transmitter. However, their applications have been limited because of the dependence on a working battery, as well as the fact that sailor location cannot be tracked beyond the one-time loss-of-signal event. In this study, a passive, person-overboard sensing concept is proposed, and the focus is to demonstrate monitoring of seawater conditions. The passive sensor was designed by incorporating a temperature-responsive hydrogel with an auxetic star pattern unit cell geometry, which transforms from a two- to three-dimensional geometry when immersed in low temperature liquid. First, the passive sensor was formed by incorporating a temperature-responsive hydrogel at opposite ends of the star pattern. The time-dependent swelling and de-swelling behavior of the copolymer hydrogel was characterized to understand the mechanical strains induced after exposure to low temperature water. Second, the design of the star pattern geometry was optimized, and artificial imperfections in the form of notches were employed to allow shape transformation to take place at appropriate and minimal applied strains. The vision is that these shape morphing structures with integrated antennas can serve as passive sensors, which, when interrogated by a remote reader, can be used to monitor how long overboard sailors have been in a low-temperature open sea environment.
Resistive Response of Carbon-Black-Epoxy Nanocomposites Upon Exposure to Moisture
Water ingress in composite structures is an extremely costly problem, and its detection before related damage begins to appear is of utmost importance. Nanocomposites, based on epoxy matrices and Carbon Black fillers (CB), are promising humidity sensors that can be integrated in composite structures during production. Nevertheless, the effects of water uptake on the electrical properties of CB-epoxy films have scarcely been reported. Therefore, this paper sheds light on the resistive response (ΔR/R0) of CB-epoxy film sensors (1.5, 2.5, 3.5 and 4.5 wt%CB) hand-coated and screen-printed on glass substrates, and then conditioned in a moist environment (~ 97 %RH). Films (1.5 wt%CB) based on a filler loading close to the Electrical Percolation Threshold (0.5-1.5 wt%CB) have shown an important increase of their resistance, most likely due to a drop of the tunneling currents. By drawing a parallel between previous work of Fauche et al. (2023) and this study, a Parallel Exponential Kinetics (PEK) model is used and describes very well the kinetics of the resistive responses (water uptake and release regimes), except for the two 2.5wt%CB sensors. In moist conditions, an equilibrium (ΔR/R0)∞ of 53.4% for the hand-coated film and 38.4% for the screen-printed film was estimated for the 1.5wt%CB sensors, and thereby revealing a stronger sensitivity to moisture than films produced by Luo et al. (2007) and Llobet et al. (2014) with a maximum ΔR/R0 of 2.5%. This suggests that CB-epoxy film sensors (≤1.5 wt%CB) have a great potential to detect high relative humidity in composite structures.
Multifunctional Soft Transducer for Electrical and Optical Sensing Applied to Fatigue Crack Monitoring
The timely discovery and monitoring of fatigue cracks on steel bridges is critical in ensuring structural safety and continuous operations. Existing sensing solutions, for example foil gauges, can be used to monitor known cracks, yet cannot be used for crack discovery because of their highly localized nature. A solution is the development and deployment of large-area electronics capable of detecting local states over large surfaces. Polymer-based nano-structured materials that respond to external impacts, such as mechanical strain, with color changing properties have attracted significant attention in structural health monitoring (SHM) community, because their passive optical/visual properties can be used to assist inspectors at quickly localizing new fatigue cracks. Here, the authors proposed a multifunction skin sensor that combines optical and electrical sensing properties. The optical function is passive and engineered to visually assist in localizing fatigue cracks, and the electrical function is added to send timely warnings to infrastructure operators. This is achieved by modifying the nanoscale structures within a photo-elastomer to obtain a soft stretchable optically-active film that is sandwiched between transparent carbon nanotube electrodes (CNT) to form a capacitor structure for adding electrical functionality. The developed sensor has a stiffness of 460 kPa and withstands reversible strain levels of up to 40%. Additionally, it exhibits a reversible and repeatable color change from light blue to deep blue, and changes the reflected color in the Vis spectra from approximately 500 nm to 600 nm and insensitive to viewing angle. The performance of the sensor is characterized through a free-standing dynamic test and further extended to a free-vibration test conducted on a steel cantilever plate. A correlation- based image processing algorithm was developed to discriminate color change and further quantify strain. The measured shift in the material’s reflection center wavelength was merged with an RGB correlations matrix and an optical gauge factor of 0.52 was obtained. An electrical gauge factor of 0.48 was obtained by subjecting the sensor to a triangular load. It was found that the parallel capacitance measurements exhibited better performance by yielding higher accuracy for free vibration strain measurements.
Recent Progress in Screen-Printed Smart Skin for Real-Time Load Detection in Structures
Civil engineering is constantly seeking innovative solutions to enhance the monitoring and maintenance of infrastructure. This is because traditional monitoring methods are often costly and time-consuming due to the use of multiple devices and manual inspections. Carbon nanotube (CNT)-polymer based self-sensing composites provide an innovative approach for structural health monitoring. Among these, composites fabricated using screen-printing techniques also offer the advantages of being cost-effective and suitable for mass production. In this study, we will manufacture a smart skin as a novel detection material for structural health monitoring by screenprinting CNT/polyurethane (PU) ink. The authors report on the optimized screenprinting design for the smart skin based on electrical characteristics and optical microscopy analysis for different mesh counts. Moreover, we investigate the electromechanical response of carbon nanotube-reinforced composite materials under impact loadings. Finally, we will present a new sensing system by applying the smart skin in a grid pattern to the structure, enabling the detection of various impacts. This innovative approach allows for the development of a versatile sensing system that can monitor and identify different types of impacts on the structure.
Experimental Investigation on Flexural Vibration Control of Large-scale Reinforced Concrete Metaplates
In this study, the vibration control performance of a large-scale reinforced concrete (RC) metaplate is experimentally validated and investigated. The local resonance frequency of plate-type LRM unit is numerically estimated. 4,200 x 3,000 x 210 mm RC metaplate, embedded with 165 LRM units measuring 200 x 200 x 85 mm each, is excited to heavy-weight impact to measure its acceleration responses. The experimental results showed that the generation of a local resonance bandgap in metaplate, leading to vibration control within the bandgap frequency range. The acceleration response of the metaplate is attenuated by up to 85.39 % at the local resonance frequency (42.51 Hz) compared to an RC plate without LRMs. The amplitude of modal peak within the bandgap frequency range is attenuated by up to 71.22 %. Based on the two indicators used to analyzed vibration control performance, The influence of both the local resonance and modal characteristics of the metaplate on its bandgap behavior is analyzed.
Multifunctional Characterization of 3D Printed Structural Battery Composites for Battery Health Monitoring
As a promising lightweight multifunctional material, carbon fiber structural battery composites have great potentials to enable longer service life and operating distance for the rapidly increasing mobile electric technologies. While simultaneously carrying mechanical loads and storing electrical energy, the developed multifunctional composites can achieve “massless” energy storage and further extend to sensing and energy harvesting for self-powered structural health monitoring. However, it is still very challenging to predict the state-of-health of structural battery composites due to a lack of understanding of underlying coupled mechanical-electrochemical phenomena during operation. In this study, we first use a novel 3D printing method to fabricate and tailor microstructure of the multifunctional carbon fiber composites. With an optimal electrode layer thickness of 0.4 mm, the stable specific capacity at 1C reaches over 80% of the theoretical capacity of the electrode active materials (lithium iron phosphate) with an average energy density of 152 Wh/kg observed. The corresponding flexural modulus and flexural strength are 8.7 GPa and 69.6 MPa, respectively. The state-of-health of 3D printed structural battery composites under electrochemical cycling and external mechanical loadings are also investigated. The mechanical performance is not affected by the electrochemical charge-discharge processes. The structural battery composites under three-point bending testing show good capacity retention with rapid degradation of electrochemical performance observed near fracture point. The findings from this study not only provide insights for monitoring the state-of-health of structural battery but also show mechanical-electrochemical coupling as a potential way of self-powered structural health monitoring through the 3D printed multifunctional composites.
Identifying the Location of Cracks in Intelligent Carbon Based TRC Elements
The goal of the study is to develop an identification procedure that identifies the location of cracks within textile reinforced concrete (TRC) elements by using their smart self-sensory capabilities. To answer this goal the investigation offers to adopt the principles of the time domain reflectometer (TDR) analysis and to explore the changes of the spectrum of the impedance. In order to use the concept, the study considers the electrical characterization of the sensory carbon yarns, mainly the dependency of the impedance with the yarn’s length and offers a calibration procedure. It was found that the procedure has the capabilities to identify all cracking events but also involved additional false alarm scenarios. It is further demonstrated that only the location of the first crack was accurately identified.
Sustainable Smart Self-Sensory Infrastructures for Leakage Detection
The study aims to develop sustainable smart self-sensory infrastructures with leakage detection capabilities. To answer this goal the study offers to combine two advanced technologies, namely: smart textile reinforcement (TR) and magnesium phosphate cement (MPC). The TR is produced with AR-glass and carbon yarns, in which the electrical conductivity of the carbon yarns enables them to be used also as smart self-sensory agents. The study presents the feasibility of the TR-MPC smart pipe concept by demonstrating its production possibilities, as well as its sensory capabilities to detect and distinguish between the magnitude of leakage events.
Development of a Fiber-Shaped MLPerovskite Sensor for Structural Health Monitoring in Composites
Composite materials are widely used in various industries due to their high strengthto- weight ratio and appealing properties. However, the detection and assessment of damage in composite structures can be challenging, requiring the use of effective structural health monitoring (SHM) techniques. This study explored the potential of an embedded fiber-shaped mechanoluminescence (ML)-perovskite sensor for real-time monitoring of impact events in composites. The sensor was fabricated by coating a perovskite photodetector with a sensing layer comprising copper-doped zinc sulfide (ZnS:Cu)/polydimethylsiloxane (PDMS) blend, using carbon nanotube (CNT) yarns as a substrate. The sensor was characterized and its ability to detect low-energy impact events, which is an important indicator of SHM, was assessed. ML materials emit light when subjected to mechanical stress, which can be detected by a perovskite photodetector and converted to an electrical current. The variations in the electrical current can be measured to provide in-situ and real-time information on the condition of the structure. CNT yarns are a suitable candidate for embedded sensors in composite structures due to their high sensitivity and exceptional mechanical properties. The flexibility of the CNT also allows the sensor to be weaved within a composite fabric, minimizing the intrusiveness effect of the embedment process. The experimental result showed that the CNT ML-perovskite sensor demonstrated good sensitivity and response time to mechanical stimuli, and a direct correlation was observed between the intensity of the electrical current and the impact energy. The ML-perovskite device showed the potential as a promising embedded sensor for impact monitoring, which could significantly contribute to the field of SHM.
Fire Safety of Rechargeable Battery Energy Storage Systems: Present and Future Prospects
The fire safety of the elements is determined by the various testing methods and procedures, which simulate the real world scenarios. Battery energy storage systems (BESS) continue to be developed and increased in size due to user demand. Simultaneously, European and global testing standards use test methods are not harmonized and use methodology of dubious accuracy and repeatability. In consequence, general concept of the fire safety in terms of battery energy storage systems can be advanced, especially under the circumstances the BESS fires are known for their high intensity, heat release, and inability to be efficiently doused. With exponentially increasing stock of the BESS in recent and upcoming years, the focus on the fire safety should be expanded. This paper aims to present the current state of testing methods in the field of fire resistance and mechanical failure of the BESS. The analysis will envelop multiple markets around the world and the major differences between them due to lack of harmonization, as well as evaluate the accuracy and repeatability of the selected method. Consequently, the article will present the future prospects and what can be done to ensure fire safety of the BESS using standardization.
Monitoring of Phase Transition in Frozen Soil Using the Nonlinear (SPC-1) Ultrasonic Technique
Frozen soils constitute a significant amount of land’s surface and are responsible for the (global) energy balances and heat transfer mechanisms affecting both land and atmospheric processes. Characterization and measurement of the properties of frozen soils during phase transition (frozen to unfrozen) is a challenging problem vowing to heterogeneity in ice-lens formation, unfrozen water, and pore structure configuration of soil grains. This study presents a non-linear ultrasonic (NLU) technique called the sideband peak count-index (SPC-I) in combination with the linear ultrasonic (LU) technique to monitor the phase transition (frozen to unfrozen) of frozen soil specimens. Earlier studies using nondestructive testing and evaluation (NDT&E) have demonstrated the use of LU and NLU techniques to detect defects such as micro-cracks, however, there are no studies performed to evaluate the use of this approach to quantify the non-linearity in frozen soils during their phase transitions. The general approach of applying the SPC-I technique is by transmitting and analyzing a single excitation signal that is propagated through the frozen soil specimen. In this study, the clayey sand soils are considered for evaluating the non-linear behavior of the soils during the phase transition process (frozen to unfrozen). These soils can undergo significant frost-heave and thaw-weakening phenomena resulting in infrastructure distress including potholes, differential settlements, slope failures, and thawing-induced landslides. In the United States approximately 2 billion dollars were spent annually to just repair the highway infrastructure distress caused due to freeze-thaw cycles and better characterization approaches are required to understand the behavior of this non-linear geomaterial. The NLU SPC-I technique combined with LU technique could robustly detect the changes in material properties of frozen soil, however, it can be further tuned and improved by exciting a tuned excitation signal.
Non-Linear Signature in Ultrasonic Pulse- Echo Signal for Structural Health Monitoring of Fatigue Induced Composite Structures
Experimental evaluation of material state damage due to frequency varied fatigue loading has been reported in this article by estimating the non-linearity in ultrasonic signals in CFRP composite structures. In recent years, researchers have been utilizing the non-linearity in Lamb wave propagation for structural health monitoring, and thus it is becoming a powerful technique for non-destructive evaluation. Unlike isotropic and homogeneous materials, the damage state modeling of composite structures is complex due to their heterogeneous arrangement of material constituents, especially in fatigue induced loading cycles. Although established for metals and alloys, the quantification of material state of carbon fiber reinforced viscoelastic polymer composites due to dynamic loading environment is still under an active research area. This research evaluates the non-linearity effect in stress relaxation using high frequency (~25 MHz) ultrasonic pulse-echo signals. A set of tensile test specimen as per ASTM standard is prepared and fatigue loads are applied at constant time intervals. During these intervals, scanning acoustic microscope is used to generate and capture pulse-echo signals. The change of second and third order non-linearity parameter of ultrasonic pressure waves are correlated with the fatigue induced internal stresses and damage accumulations. Initial investigation indicates that the higher order non-linearity is a function of applied fatigue loading frequency and the remaining life of the composite structure.
Ultrasonic Guided Wave Imaging Method Based on Particle Swarm Optimization for Composite Damage Monitoring
Due to the dispersion and multi-mode characteristics of ultrasonic guided waves, many current imaging techniques based on ultrasonic guided waves are prone to artifacts and large imaging spots when they are used for structural damage detection. Therefore, an ultrasonic guided wave imaging method based on particle swarm optimization (PSO) is proposed in this paper to transform the problem of damage imaging into a problem of damage source search. A fitness function for damage imaging location is proposed. The smaller the difference between the travel time of the scattering signal and the travel time of the particle, the larger the amplitude assigned to the particle. The total amplitude of the particle after superposition of all sensing paths is used as the fitness function. The particle with the largest total amplitude is searched for by the PSO algorithm to achieve damage localization. The experimental results show that the proposed method has successfully detected the crack damage of composite and achieved a better imaging effect. It has higher positioning accuracy compared to other imaging methods.
Nonlinear Guided Wave Mixing for Fatigue Damages Identification in Steel Pipe
Nonlinear guided wave mixing techniques have been widely used for material degradation characterization in solids. In this study, a method using nonlinear guided wave mixing is proposed to identify fatigue damages, including both material degradation and subsequent fatigue crack propagation, in steel pipes. By means of theoretical analysis, two incident waves are selected to generate combined harmonics in the damaged pipes. Based on finite element (FE) simulations, the combined harmonic at difference frequency is successfully observed, and the nonlinear index, β, based on the combined harmonic at difference frequency is found to be capable of identifying both material degradation and the subsequent fatigue crack. This study provides a promising method for fatigue damages identification in the early stage using nonlinear guided wave mixing approach.
Assessment of Intermetallic Compounds within Dissimilar Joints Using Nonlinear Guided- Wave Features
The welding of dissimilar materials finds a wide variety of applications in the fields of industrial construction and manufacturing. One of the most prominent challenges of these techniques is the development of intermetallic compounds (IMCs) at the joint level, which reduces the strength of the welds. Nonlinear ultrasound detection methods are used as modern non-destructive testing tools for inspecting early damage in various materials. This study investigates the assessment of the thickness of the IMC layer within dissimilar joints using nonlinear guided-wave (GW) features. A numerical investigation was performed on friction-stir-welded (FSW) lap joints, between AA5052-H32 aluminum and ASTM 516-70 steel, with various intermetallic conditions/thicknesses. GWs were excited in the specimens using piezoelectric transducers placed on the aluminum side. The second-harmonic generation (SHG) among the fundamental GW modes was examined within the low-excitation-frequency range. It was found that the relative acoustic nonlinearity parameter ?′ varies linearly with the thickness of the IMC layer. The attained results prove the sensitivity of GW nonlinear features to microstructural variations, within dissimilar FSW joints, and demonstrate the capability of LWs in accurately scrutinizing their strength. Keywords: Friction stir welding, dissimilar welds, intermetallic compound, nonlinear guided waves, acoustic non-linear parameter, numerical modelling.
Closed Form Solution for SHM-Based Bayesian Reliability Assessment
Extensive structural health monitoring (SHM) of civil infrastructure provides massive amounts of data that must be promptly and effectively analyzed through appropriate models to infer the health state of structures. In general terms, the health state of a structure can be expressed as its structural reliability with respect to the most significant limit states. When the reliability is estimated based on SHM data, the problem is typically solved numerically with iterative approaches, which are computationally expensive and do not allow early warning and prompt response. This work presents a logically consistent approach for the Bayesian estimation of structural reliability based on SHM observations. Moreover, it illustrates how closed-form solutions can be obtained using linear models and Normal random variables. The proposed approach can effectively evaluate the sensitivity of structural reliability with respect to SHM observations; this can be used as a novel performance index for monitoring systems. Finally, this paper proposes an application of this approach to a real-life case study, the crack opening monitoring of the Settefonti highway viaduct in Italy.
Metrological Validation of Non-Destructive Testing Techniques for Assessing the Condition State of Pre-Stressing Systems
The number of prestressed reinforced concrete bridges exceeding their expected lifespan is constantly increasing, leading to a growing interest by infrastructure managers and control authorities in monitoring and maintaining them. Accurate knowledge about the general degradation of bridges is necessary for effective planning and prioritization of maintenance intervention and structural monitoring. The prestressing system of a bridge is a particularly sensitive component; its condition state plays a central role in risk assessment and bridge maintenance. Non-destructive, semidestructive, and destructive tests are recommended to evaluate the prestressing system's condition state by many guidelines. Several innovative investigation techniques are currently available on the market; however, their actual effectiveness is still debated and requires further investigation. This work aims to verify the effectiveness of the following non-destructive tests (NDTs): Digital Radiography, Ground Penetration Radar, and Reflectometric Impulse Measurement Test. These NDTs have been performed on the Alveo Vecchio viaduct, which is a decommissioned highway bridge now used as an open-air laboratory. These NDT’s effectiveness is studied through correlation analyses performed by cross-referencing the NDT outcomes and the corresponding intensity levels of defects directly observed during a controlled demolition of the deck beams. This study provides significant insight into the accuracy level of non-destructive tests and is part of an extensive experimentation campaign.
Natural Frequency and Displacement Ratio Based Probabilistic Damage Identification for Bridges Using FE Model Update
This paper proposes a natural frequency and displacement ratio-based probabilistic damage identification method for bridges using the finite element (FE) model update. When the damage location is known, it can be detected from an appropriate damagesensitive feature (DSF). However, damaged components are seldom known before inspections. This makes it difficult to find an appropriate DSF and damage identification is sometimes challenging. This paper aims to propose a method to solve this issue by integrating multiple DSFs, natural frequencies and displacement ratio, as a decisionlevel data fusion approach. They are complementary in terms of sensitivity to damage. In addition, probability density functions (PDFs) of structural parameters are estimated from PDFs of observed DSFs through the FE model update to consider errors and uncertainties in measurement data. An in-house model bridge experiment is carried out to investigate the feasibility. The results demonstrated that the two kinds of damages in a bearing and girder reproduced in the experiment were successfully identified without false positives even when these damages simultaneously occurred.
Inverse Reconstruction of Unsteady Aerodynamic Loads Acting on Railway Vehicles
During normal operation, railway vehicles often endure significant vibrations due to unsteady aerodynamic loads. Precisely quantifying these transient forces offers essential insights for operational safety monitoring and vehicle aerodynamic testing. In this paper, we introduce an innovative inverse method for reconstructing active aerodynamic loads using a limited number of acceleration measurements. This method capitalizes on health monitoring instruments already present on the vehicles, thereby eliminating the necessity for supplementary pressure sensors on the vehicle’s exterior surface, as mandated by traditional direct pressure measurement strategies. We develop a Multi-Task Gaussian Processes (MTGP) inverse estimation technique to calculate the conditional probability distribution of loads given the noise-affected acceleration data. The MTGP approach boasts the advantage of analytically forming the posterior of unsteady aerodynamic loads at any time point, as well as offering high reconstruction accuracy. To validate our proposed method, we utilize a numerical example with a 31 DOF railway vehicle model. Aerodynamic loads generated by two trains passing each other are applied to the vehicle model, and acceleration data from the bogies are employed for the inverse reconstruction process. Our results successfully demonstrate the feasibility of reconstructing unsteady aerodynamic loads on railway vehicles, highlighting the potential of our novel approach.
Hybrid Probabilistic Deep Learning for Damage Identification
In structural health monitoring, various types of sensors collect a large amount of data for structural defect detection. These data provide critical support for the application of machine learning for structural damage identification. However, machine learning relies heavily on training data, whose quality and distribution can affect the effectiveness of detection models in real-world damage identification. In addition, machine learning contains a large number of parameters that are highly uncertain, which results in the output of machine learning models is not always as reliable. These deterministic deep networks usually make overconfident decisions in some data. The ability of deep learning to provide safe and reliable decisions is very important when applied in the field of engineering. In order to ensure the decision security of machine learning models, this paper proposes a hybrid probabilistic deep network for structural damage identification. The proposed method converts deterministic weights into a Gaussian distribution, which in turn quantifies the uncertainty in machine learning. Among them, variational inference is used for uncertainty modeling of probabilistic deep networks. These uncertainty metrics can be used to determine whether the output of the machine learning model is reliable. Nevertheless, the introduction of uncertainty weakens the learning ability of deep networks. Meanwhile, the number of parameters in the probabilistic layer is twice that of the deterministic layer for the same architecture. Therefore, probabilistic deep learning is more difficult to train compared to deterministic deep learning. To address these issues, deep learning with hybrid probabilistic and non-probabilistic layers needs to be investigated. This paper analyzed and discussed the effects of different numbers of probability layers on the effectiveness of structural damage identification. Finally, a series of experimental results showed that the proposed method is able to accurately identify structural damage while quantifying the decision uncertainty.
A Comparison of Value-based and Policy-based Reinforcement Learning for Monitoringinformed Railway Maintenance Planning
Optimal maintenance planning for railway infrastructure and assets forms a complex sequential decision-making problem. Railways are naturally subject to deterioration, which can result in compromised service and increased safety risks and costs. Maintenance actions ought to be proactively planned to prevent the adverse effects of deterioration and the associated costs. Such predictive actions can be planned based on monitoring data, which are often indirect and noisy, thus offering an uncertain assessment of the railway condition. From a mathematical perspective, this forms a stochastic control problem under data uncertainty, which can be cast as a Partially Observable Markov Decision Process (POMDP). In this work, we model the real-world problem of railway optimal maintenance planning as a POMDP, with the problem parameters inferred from real-world monitoring data. The POMDP model serves to infer beliefs over a set of hidden states, which aim to capture the evolution of the underlying deterioration process. The maintenance optimization problem is here ultimately solved via the use of deep Reinforcement Learning (RL) techniques, which allow for a more flexible and broad search over the policy space when compared to classical POMDP solution algorithms. A comparison of value-based and policy-based RL methods is also offered, which exploit deep learning architectures to model either action-value functions (i.e., the expected returns from an action-state pair) or directly the policy. Our work shows how this complex planning problem can be effectively solved via deep RL to derive an optimized maintenance policy of railway tracks, demonstrated on real-world monitoring data, and offers insights into the solution provided by different classes of RL algorithms.
Deep Learning based Pothole Monocular Depth Estimation and Segmentation Using 3D Scanner-Derived Depth Maps
Potholes pose significant safety risks to drivers and cause damage to vehicles. This paper modified a novel approach called the monocular depth estimation and segmentation (modified 3DPredicNet) network [1] to accurately estimate depth maps and segment potholes. To facilitate model training and evaluation, a comprehensive dataset consists of RGB images captured using a DSLR camera and corresponding 3D scan data for generating depth maps. The depth maps derived from the 3D scans are utilized for pothole depth estimation, while masks are used for pothole segmentation. The evaluation results reveal the model's ability to accurately predict and segment potholes in RGB images, achieving a minimum absolute relative error (ARel) of 0.062, square relative error (SRel) of 0.011, and root mean square error (RMSE) of 0.118 when tested on the newly developed dataset. Moreover, when tested on the newly developed dataset, the model demonstrates good pothole segmentation performance, attaining a high mean intersection over union (mIoU) of 81.05. Furthermore, when utilizing the publicly available dataset, the modified 3DPredicNet achieved accurate depth estimation with ARel of 0.093 and SRel of 0.033.
On Metrics for Information Value Quantification
In this paper, we aggregate, analyze, and exemplify several metrics for the information value. The metrics vary by absolute value, normalizations, or full probabilistic quantification. The normalization of the information value encompasses the division by (1) the expected and maximized utility of the base scenario (usually without additional information), (2) by the system performance, or (3) by the information acquirement costs. The latter can be aligned to the return over investment ratio to support and facilitate SHM system investment decision. With a case study on malevolent attacks to a generic iconic bridge, we exemplify the information value metrics and point to the application ranges and the implications of full distribution considerations.
Enabling Robustness to Vehicle-Bridge Variability in Drive-By Bridge Health Monitoring through Physics-Informed Signal Decomposition
Bridge health monitoring (BHM) is important due to its benefits in detecting and diagnosing potential damages to bridges, providing early warning signals, and guiding maintenance decisions. Compared to manual inspection and fixed-sensors-based monitoring, drive-by BHM leverages vibration responses measured from vehicles passing over bridges to indirectly diagnose bridge damages, offering a rapid, mobile, and economical complementary solution. However, vehicle-bridge interaction (VBI) systems have large variations in bridge configurations, vehicle suspension systems, driving speeds, and so on, making it challenging to develop a damage diagnosis algorithm that is robust to vehicle-bridge variability. Moreover, existing approaches often require vehicle speed within a specific range to provide both informative and reliable signals, limiting their practical applications. To address these challenges, we introduce a damage diagnosis approach that extracts damage-sensitive features and enhances their robustness to vehicle-bridge variability through physics-informed signal decomposition. Our approach first pre-processes and decomposes the vehicle vibration signal using the synchro-squeezed wavelet transform (SWT) because of its anti-noise property and its ability to represent the non-stationary and time-varying signals from drive-by vehicles. Then, a damage-sensitive signal is reconstructed using the inverse SWT within a physics-informed frequency band which excludes the vehicle and bridge resonances while keeping the damage-sensitive information. Peak features, such as peak location and energy, are input to the Gaussian Mixture Model clustering algorithm for diagnosing bridge damage in an unsupervised fashion. The performance of the proposed approach is evaluated on a numerical VBI model, which includes three vehicle types, six bridge lengths, and three bridge cross-sections, and takes into consideration the variability in vehicle properties and speed. The results validate that the extracted features are damage-sensitive and robust to various vehiclebridge systems, achieving an overall mean absolute percentage error of 0.78% for damage localization and 13.13% for damage quantification.
Explainable Machine Learning Framework for Guided Waves Signal Reconstruction and Structural Health Monitoring Under Varying Operating and Environmental States
Lamb Wave (LW) signal has been widely used as a technique for damage identification and localization in Structural Health Monitoring (SHM) due to its sensitivity to varying types of state changes. Analyzing and constructing the guided wave signals then become a critical step in damage detection and assessment. Researchers have discovered features in both temporal and frequency domains for signal description and reconstruction. Yet the related features are challenging to be manually developed as the stochastic acoustic signals captured by sensors can be complex and are determined by various factors such as complex boundary conditions and material properties. Recently, neural network has exhibited the capability for time series reconstruction yet lacking of interpretability. In this study, a convolutional autoencoder (CAE) network has been developed to compress the information of the collected signals along with parameters such as damage levels and external loads into time-invariant and time-variant latent spaces at the bottleneck layer which can be easier to analyze and more efficiently used for state estimation and signal reconstruction. The power of estimating signal and its corresponding conditions has been examined by combining a feed forward neural network (FFNN) with the encoder or decoder extracted from the CAE network so that states of raw signals can be predicted and signals under known states can be reconstructed. The proposed framework has been applied in two test cases to verify its capability and stability in terms of different latent space types. The experiment was conducted on an Al plate under different damage states with PZTs serving as actuators and receivers. It is shown that the state parameters can be estimated with high accuracy and the signals can be generated with low error and thus alleviate the requirements of time-consuming experiments.
A magnetic object subject to an external rotating magnetic field would be rotated due to the alignment tendency between its internal magnetization and the field. Based on this principle, 12 shapes of swimming microrobots around 1mm long were designed and 3Dprinted using biodegradable materials Poly (ethylene glycol) diacrylate (PEDGA). Their surface was decorated with superparamagnetic iron oxide nanoparticles (SPIO NPs) to provide magnetic responsivity. An array of 12 permanent magnets generated a rotating uniform magnetic field (~ 100 mT) to impose magnetic torque, which induces a tumbling motion in the microrobot. We developed a dynamic model that captured the behavior of swimming microrobots of different shapes and showed good agreement with experimental results. Among these 12 shapes, we found that microrobots with equal length, width, and depth performed better. The observed translational speed of the Hollow Cube microrobot can exceed 17.84 mm/s (17.84 body lengths/s) under a rotating magnetic field of 5.26 Hz. These microrobots could swim to the targeted sites in a simplified vessel branch. And a finite element model was created to simulate the motion of the swimming microrobot under a flow rate of 0.062 m/s.
Ultrasonic Connected Probe for In-Service Monitoring of Pressure Equipment
In order to guarantee the safety of the gas production plants, Air Liquide conducts NDT inspections regularly closely following the assets’ maintenance plans. One high criticality asset is the Pressure Swing Adsorber (PSA), found in the last stages of the hydrogen production process, where product purification takes place. These adsorbers are pressure vessels and need to be inspected for hydrogen enhanced fatigue cracks. The PSA adsorbers are inspected for cracks regularly by INTACT following a dedicated inspection plan. When indications or cracks are found, a Fitness For Service (FFS) assessment is done to determine the stability of the crack, and if stable, propagate the crack over time calculating the number of cycles before the crack reaches its critical size, in other words, the time up until this crack will go through the complete thickness of the adsorber creating a leak. As an output of the FFS assessment, certain cracks could require a close monitoring, with high frequency intervals of inspection, this necessity is what drove Air Liquide R&D and INTACT to partner up to develop the “Ultrasonic connected probe for in-service monitoring of pressure equipment”.
Study of the Guided Wave Sensing by Hybrid Piezoelectric-FBG Approach
In this research, analysis of ultrasonic guided wave sensing using fiber optic strain sensors based on fiber Bragg gratings (FBG) is presented. Wave excitation is based on piezoelectric transducers. Sensitivity of the FBG sensors to propagating ultrasonic waves plays an important role in the design process of SHM system based on fiber optic sensors. The presented study has experimental character. Separation of symmetric and antisymmetric modes is studied based on simultaneous sensing of the guided waves with two FBG sensors. The influence of actuation is studied for various frequencies of excitation. Moreover, influence of piezoelectric transducers with different diameters on the wave signals registered at the FBG sensors is investigated. The obtained results show significant impact of the diameter of the piezoelectric actuators on the guided waves signals registered by FBGs.
Ultrasonic Multi-Hole Imaging Using Full Waveform Inversion
Ultrasound computed tomography (USCT), in recent years, is becoming an increasingly popular method for structural health monitoring (SHM) and non-destructive evaluation (NDE). Full waveform inversion (FWI), a cutting-edge inversion method, utilizes all information in ultrasonic measurements in USCT. It iteratively reconstructs the model parameters (i.e., wave speed of materials) of the scanned specimen by calculating the gradient of waveform difference between the measured and synthetic signals through a partial derivative equation (PDE)-constrained optimization process. By reconstructing these model parameters, defects inside the specimen can be identified. This study aims to evaluate FWI’s performance in imaging defects (i.e., holes). FWI was implemented to process the numerically generated signals to simulate the data acquisition in a steel specimen with six holes of different sizes (diameters ranging from 0.4 mm to 5.2 mm). First, a shorter numerical model with the same number and size of holes was used to benchmark FWI’s performance with full coverage of source and receiver elements for the domain. To emulate large-scale structural component inspection, the numerical domain was then changed to the dimensions of the actual steel specimen, and the scanning setup was changed to a pair of linear transducer arrays with relatively shorter apertures located on the top and bottom of the specimen. The effect of the array aperture on the FWI performance was studied. The reconstructed model parameter, longitudinal wave speed (Vp) , and shear wave speed (Vs) maps showed that with the current setup, FWI can identify the locations of all holes. A new imaging condition was proposed using the inverted Vp and Vs maps to quantify the shapes and size of the holes. The ultrasound signals from the actual steel specimen with holes of the same dimensions were also analyzed and modeled. The results exhibit the potential of applying FWI on experimentally acquired data from the steel sample.
Defect Identification in Concrete Using Deep Learning Technique on Nonlinear Ultrasonic Wave
Nonlinear ultrasonic techniques have emerged as a promising tool for detecting defect in concrete. However, detecting the first and higher harmonics of ultrasonic waves in concrete can be challenging due to its complexity and heterogeneity nature characteristics. In this study, a deep learning algorithm was used to improve the accuracy of defect identification. Experiments were conducted on three concrete block samples, including pure concrete and two concrete samples with inclusions. The study utilized an array of R6 sensors as transmitters and an array of R15 sensors as receivers for the measurements. The deep learning algorithm was applied to the wavelet spectrogram of each wave, using 1050 images for training, 116 images for validation, and 292 images for testing. Convolutional neural networks (CNN) were used in the deep learning model. The approach focused on the regions in the first and second harmonic, which are more representative of defect, in the deep learning method. The proposed network consists of several layers that perform different operations to extract relevant features from the input data. The experiments demonstrated the effectiveness of using deep learning algorithms for identifying and classifying defect in concrete. The model achieved an overall accuracy of 94.8% in detecting defect in the concrete samples, with a high precision score for both defect and no-defect identification. This approach successfully detected defect in concrete samples, including the presence of inclusions. As a result, the study showed that deep learning algorithms can be effective in identifying and classifying defect in concrete, with the potential to improve the maintenance and management of concrete structures, enhancing their safety and durability.
Experimental Study on Debonding Detection for Grouted Jacket Connection of an Off-Shore Wind Turbine Supporting Structure Specimen with PZT Patches
Grouted jacket connections (GJCs) have been widely used to connect longitudinal components of offshore wind turbine supporting structures. Debonding defects detection in GJCs is critical for the safety and serviceability of offshore wind turbine support structures. In this paper, an interface debonding detection approach for a scaled GJC specimen using both surface stress wave and Electromechanical impedance (EMI) measurement is proposed with Piezoelectric lead zirconate titanate (PZT) patches. The scaled GJC specimen with various mimicked interface debonding detects is designed. Based on the surface stress wave measurements of PZT sensors arranged with a one pitch and multiple catch (OPMC) configuration, the regions of the mimicked defects are estimated using the intersections of the surface stress wave propagation paths with abnormal measurements. EMI measurements of PZT sensors located in the estimated debonding regions are used to accurately identify the dimension of the mimicked debonding defects using the root mean square deviation (RMSD) index of the impedance measurements of PZT sensors. The visualized debonding region is close to the actual location of the mimicked debonding defect.
Multi-Scale MT-InSAR Techniques for Structural Health Monitoring: Applications to the Italian Cultural Heritage Context
In recent years, the number of applications of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques for structural health monitoring (SHM) purposes has constantly raised. One of the main advantages that MT-InSAR can provide is the possibility to analyze wide areas, such as entire cities or archaeological sites. Thus, several interferometric algorithms have proven to be particularly valid when applied in urban contexts, considering the high reflectivity provided by the structures. In addition, the growing availability of high-resolution SAR satellite constellations, such as the Italian COSMO-SkyMed and the German TerraSAR-X, allows obtaining significantly accurate measures and detecting high density of Measurement Points (MP). Consequently, sufficiently detailed information on single structures can be recorded, permitting to perform both urban scale and local scale analyses. On the other hand, several drawbacks are still present, mostly related to the hardly post-processing processes needed. Indeed, beside the technical difficulties that can be encountered during the analysis (e.g. geocoding errors and noisiness in the time series), an expert interpretation of the results is still required to avoid misinterpretation of data. Anyway, considering both advantages and disadvantages of the technique, MT-InSAR undoubtedly represents a very cost-effective tool in the structural monitoring field. In this paper, MT-InSAR applications for structural monitoring on the Italian Cultural Heritage context are presented. The analyses have been conducted at both urban and local scale, processing images acquired by COSMO-SkyMed constellation in Stripmap mode (~3 meters resolution). First, spatial interpolation algorithms have been implemented to estimate the overall deformations at urban scale. Subsequently, the attention has focused on some of the main cultural assets of the case-study cities, investigating the MPs detected on the structures themselves. In this work, three cities are presented as case-studies: Verona, Padova (North Italy), and L’Aquila (central Italy). For each city, a specific Cultural Heritage structure has been selected for the local scale analysis, namely the Roman Arena in Verona, the Scrovegni Chapel in Padova, and the Civic Tower in L’Aquila. All these monuments have been monitored by traditional SHM techniques for years, with the possibility to correlate and validate onsite and satellite data.
Remote Structural Health Monitoring of Concrete Bridge Using InSAR: A Case Study
Structural Health Monitoring (SHM) provides real-time information about structural damage and its evolution over time, which supports decision-makers in managing structural integrity. However, traditional SHM systems, such as vibrationbased systems with accelerometers, require laborious installation and maintenance of instrumentation, including sensors, cables, and acquisition systems directly installed on the monitored structure. Recently, remote sensing through active satellites, such as new Synthetic Aperture Radar (SAR) missions, offers a promising alternative for monitoring infrastructure assets. This paper explores the use of SAR Interferometry (InSAR) processing to monitor the health of bridges. The full interferometry processing chain is based on Sentinel-1 and CosmoSKYMED synthetic aperture radar data and a combination of open-source routines from the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). The Permanent Scatterer Interferometry SAR (PS-InSAR) technique is used to derive displacement time histories. The applicability of this technique is evaluated on a concrete bridge located over the Oglio River in Isola Dovarese, Italy, which has GNSS receivers installed. To validate the methodology, PS-InSAR derived time histories are compared with GNSSderived data, and both techniques are used to quantify bridge displacement rates from October 2020 to August 2022.
Satellite Monitoring of Transportation Infrastructure
Harsh weather conditions caused by climate change and increased traffic loads can accelerate the aging process of core public infrastructure. Conventional condition assessment methods primarily rely on visual inspections conducted years apart, making it difficult to detect subtle, ongoing changes in performance resulting from structural deterioration. Consequently, engineers may be unable to initiate early countermeasures to prevent service disruption or structural failure. In response, public infrastructure owners are actively seeking innovative solutions that can help maintain high levels of user safety, reduce service disruption, extend the service life of infrastructure, and lower overall life-cycle costs. To this end, the National Research Council Canada, Transport Canada, and Infrastructure Canada have collaborated for several years to adapt, further develop and validate space-based earth observation technology for monitoring key public infrastructure, including bridges and, more recently, marine ports and airports. Case studies on runways of the Vancouver International Airport and wharves of the Vancouver Fraser Port have been conducted to validate remote satellite observations with in-situ surveying subsidence measurements. Satellite image interferometry allows the mapping of displacement by determining the signal phase change within pairs of co-registered pixels from two radar images of the same target taken at different times. The result is a line-of-sight (LOS) displacement measurement that can be used to remotely assess uplift, subsidence, and horizontal motion taking place over time. Since it is a 1D measurement taken at an angle from the zenith, the vertical and horizontal components are unknown a priory. Thus, attempting to validate satellite LOS measurements with in-situ surveying vertical measurements can be challenging since one has to make valid assumptions about the horizontal movements of the ground targets being measured – in our case, the riding surface of the airport runways or the port wharves. This paper compares satellitemeasured displacements and field survey measurements at selected locations and discusses challenges associated with the interpretation of satellite measurements.
Satellite Multispectral and Infrared Imagery Analysis to Contextualize Bridge Structural Health Observations – A study of the Samuel de Champlain Bridge in Montreal, Canada
In 2021-2022, two related validation studies were conducted on structural health monitoring of the Samuel de Champlain Bridge in the La Prairie Basin of the Saint Lawrence River near Montreal, Canada. In the first study, C-band satellite Interferometric Synthetic Aperture Radar (InSAR) observations of bridge line-ofsight thermal displacement measurements were compared to predicted values. A companion observational study of multispectral (MS) imagery assessed turbulent and flow features on the river’s surface that are indicative of changes in the riverbed morphology and used stereo MS imagery to derive river velocity vectors using a Particle Image Velocimetry (PIV) algorithm. This paper presents the preliminary findings of a follow-up study, in which timeseries of InSAR displacement observations at nine piers of the Samuel de Champlain Bridge were compared to water depth, ambient water surface temperature from satellite imagery, and ambient air temperature from a nearby in-situ weather station.
Technical Guidance on Satellite-Based Deformation Monitoring of Bridges
Since 2014, the National Research Council Canada (NRC) has led partnerships in which the application of radar satellite imagery for bridge deformation monitoring has been validated in five major case studies (including the North Channel Bridge in Cornwall (ON), the Jacques-Cartier, Victoria and Samuel de Champlain bridges in Montreal (QC), and the Confederation Bridge leading to Prince Edward Island (PEI). Based on the knowledge and experience developed in these bridge case studies, a set of technical guidelines is summarized and presented in this paper, which discusses the characteristics of bridges and the satellite parameters that can affect and improve the quality of satellite-based bridge deformation monitoring. The overarching goal of these guidelines is to enable more efficient and accurate monitoring of bridges using radar satellite imagery by recognizing the limitations of the technology and by better understanding which bridge and satellite parameters can optimize its application.
C-GMS, The Canadian Ground Motion Service-Assessment and Correction of Thermal Effects on Bridges
Many public infrastructure assets in Canada are old and deteriorating, with almost 40% of publicly-owned bridges rated as fair, poor, or very poor in terms of their condition. Structural health monitoring (SHM) is a method for ensuring the safety and integrity of structures, detecting damage progression, and estimating the performance of infrastructure assets over time. For this purpose, a new wide-area Canadian Ground Motion Service has been created to make radar satellite-measured ground motion data publicly available. In this paper, the methodology of the approach is described with a focus on the assessment and correction of thermal effects on bridges.
Coarse-to-Fine Seismic Assessment Based on Computer Vision
Recently, remote sensing satellites, unmanned aerial vehicles (UAVs), and smartphones have been extensively utilized in non-contact post-earthquake inspection at different scales with cutting-edge computer vision and machine learning techniques. This study establishes a computer-vision-based coarse-to-fine seismic assessment framework to localize dense buildings in urban areas, classify collapsed and noncollapsed states, recognize multi-type surface damage on structural components, and evaluate seismic performances. First, a Transformer-CNN deep learning architecture is designed for semantic segmentation of dense buildings and binary classification of collapsed states using large-scale remote sensing satellite images. It consists of a Swin Transformer encoder, multi-stage feature fusion module, and UPerNet decoder to extract global correlations and local features of dense buildings synchronously. Then, a multi-task learning strategy is proposed to simultaneously recognize multi-type structural components (column, beam, wall), seismic damage (concrete crack, spalling, and rebar exposure), and multi-level damage states (minor, moderate, major) using medium-scale UAV images. It contains a CNN-based encoder-decoder backbone with skip-connection modules and multi-head segmentation subnetworks for different tasks. The geometric consistency loss of split line, area, and curvature is further designed to refine the semantic segmentation of local details, increase boundary smoothness, and suppress inner voids. Finally, a machine learning neural network is established to quantify the seismic damage index of structural components using damage-related parameters (lengths, areas, and numbers of concrete crack, spalling, and rebar exposure) and design-related parameters (axial compression ratio, shear span ratio, and volumetric stirrup ratio) as inputs. A seismic damage indicator with an explicit bound of [0,1] can be obtained to reflect the nonlinear accumulation of seismic damage. The effectiveness and applicability under real-world post-earthquake scenarios have been validated by the 2017 Mexico City Earthquake M7.1, 2008 Beichuan Earthquake M8.0, 2010 Yushu Earthquake M6.9, 2015 Nepal Earthquake M7.8, 2016 Ecuador Earthquake M7.8, and 2016 Meinong Earthquake M6.7.
Dynamic Behavior of Cabril Dam: Finite Element Model Calibration, Structural Health Monitoring (2008–2023) and Seismic Safety Assessment
This paper presents a complete study on the dynamic behavior of the 132 m high Cabril arch dam (Portugal). In operation for almost 70 years, horizontal cracks appeared on the downstream face after the first reservoir filling, and in the 1980s the dam started showing signs of concrete swelling. To address the concerns about its long-term safety, a pioneering continuous vibration monitoring system, designed for Seismic and Structural Health Monitoring, was installed in Cabril dam in 2008. In this work, the vibrations recorded under ambient/operational conditions for more than a decade are analyzed to estimate the modal parameters of the dam, which are then used to calibrate and validate a finite element model of the dam-reservoir-foundation system with the horizontal cracking. After that, the dynamic behavior of Cabril dam is simulated for the next decades, considering a computationally generated scenario of progressive damage due to concrete swelling. The results show that a) the dynamic behavior of Cabril dam is not being affected by the existing swelling phenomenon, and b) vibration-based analysis can be effective for detecting structural changes due to progressive damage. Lastly, the calibrated model is adapted for conducting non-linear seismic simulations, considering joint movements and tensile and compressive concrete damage. A method based on Endurance Time Analysis method is applied to assess the seismic safety of the dam with respect to the Operating Basis Earthquake and the Safety Evaluation Earthquake. The results confirm that Cabril dam can withstand accelerations several times greater than both earthquake levels, showing its adequate seismic capacity.
Damage Assessment in a Reinforced Concrete Structure under Quasi-Static Shear Loading Using OFDR-based Fibre-Optic Distributed Strain Monitoring
EDF and CEA have established an experimental program to improve knowledge of the behavior of wall-slab junctions under both out-of-plane bending and in-plane shear. The program involved reduced-scale mock-ups made of reinforced concrete (RC) that are representative of the structural elements of nuclear plant buildings. In parallel to vast experimental campaigns, numerical models of the junction under study are derived and calibrated in order to improve the computation of building responses under seismic excitations. The analysis of damaging process of these structures is reproduced by nonlinear numerical simulations, realized within the Finite Element framework CAST3M developed at CEA (www-cast3m.cea.fr/) and code Aster (https://code-aster.org), which account for steel plasticity and concrete damage, including crack propagation based on loading conditions and history. Of all the distributed techniques applicable to the Structural Health Monitoring (SHM) of RC structures, Optical Frequency-Domain Reflectometry (OFDR) is well suited because of its high spatial resolution. The OFDR principle relies on swept-wavelength homodyne interferometry. Light from a tunable laser source is split and sent through sensing and reference fibers, both being arms of an interferometer. The backscattered light recombines at an optical detector and an interferogram is recorded as the laser frequency is tuned. The spectral-domain signal is then Fourier-Transformed to yield the backscattering profile along the fiber. Finally, the fiber is segmented into successive centimeter-long gage lengths and a cross-correlation procedure provides the strain profile with respect to a reference state. We implemented the OBR4600 OFDR device from Luna Innovations. The OBR 4600 is a single channel device that provides static distributed strain monitoring over a range of 70 m, with an accuracy in strain of ±5 μm/m, a spatial resolution (gage length) of about 5 mm, and a readout time of typically 10 seconds. On the last mock-up of the experimental program, the jacks applied quasi-static displacements ranging from 0.69 mm to 11.49 mm, yielding progressive structural damage and eventually reaching concrete crack and steel plasticity. Strain profiles and natural frequencies were determined and compared to the modeling. Distributed Fiber Optic Sensing (DFOS) results provide access to continuous strain distributions along the instrumented rebars, localize deficiencies and local deformations around rebar crossings and also the emergence of concrete degradations in the joint. The OFDR technique enables highly reliable in-situ SHM of damage mechanisms within concrete structures, providing effective data for model verification and validation used in safety-related structures assessment.
Seismic Resilience Assessment of Instrumented Buildings: A Performance-based Monitoring Approach
This paper presents a novel probabilistic performance-based monitoring approach for high-resolution seismic damage assessment and resilience quantification in instrumented buildings. The objective is to estimate seismic loss and functionality metrics, which can be integrated into multi-disciplinary community resilience models consisting of interdependent physical, social, and economic metrics. The proposed methodology begins with optimal sensor placement to measure the seismic response of an instrumented building. Then, a structural model-sensor data fusion is implemented using a nonlinear model-based observer to obtain reconstructed engineering demand parameters (REDP) and their estimation uncertainty. The mean and dispersion of REDPs are employed to quantify probabilistic seismic loss consistent with FEMA P-58 methodology, estimating downtime and total loss, which are two necessary parameters for creating component-based functionality curves. These curves define resilience as a function of loss due to extreme events and provide the necessary metrics for linking engineering models with social and economic impact models to estimate multi-disciplinary resilience metrics. The proposed methodology is demonstrated using data obtained from full-scale seismic testing of a wood-frame building conducted at the E-Defense facility in Japan.
Strain Sensing in Thin Composite Laminates with Embedded Fiber Bragg Grating Sensors
Ultra-thin fiber Bragg grating sensors were used to measure strain changes in thin composite laminates. The first part of the paper presents tension tests on thin composite laminates with embedded ultra-thin fiber Bragg grating sensors to measure the gage factor relating shifts in the fiber Bragg grating reflected wavelength to the laminate strains. The second part of the paper then investigates the ability of these embedded fiber Bragg grating sensors to accurately measure the internal strains of thin laminates in bending.
A Multi-Frequency Virtual Time-Reversal Technique for Impact Localization on Stiffened Woven Composite Structures Using A0 Mode Lamb Wave
Woven composite structures play an important role in the engineering field. Due to their poor impact resistance, passive structural health monitoring (SHM) techniques for impact localization have attracted much attention. In this study, A multi-frequency virtual time-reversal technique for impact localization on stiffened woven composite structures using A0 mode lamb wave is proposed according to the time reciprocity principle. A sparse piezoelectric sensor network mounted on the structure is used to receive impact response signals. The received impact response signals are decomposed into multi-frequency narrowband wave components using the continuous wavelet transform. Virtual time-reversal is performed for each frequency narrowband wave component, multiple frames of the wavefield image corresponding to different wave speeds in a wave speed interval are reconstructed separately at the moment of impact occurrence. The maximum field value is searched for all frames of the wavefield image, and the wave speed corresponding to the maximum field value is identified as the group velocity of the A0 mode lamb wave at that frequency, and the image in which the maximum field value is located is considered as the effective imaging result at that frequency. The focus level of the effective imaging results for each frequency is calculated and used to determine whether the imaging results are retained. Finally, image fusion for impact localization using all retained imaging results is realized. The proposed method has the following advantages: (a) it can be applied to stiffened plate structures; (b) it doesn’t require any prior knowledge of structure, which is attractive for passive monitoring systems; (c) it minimizes the interference of noise and locates impact events automatically in multiple time-frequency domains; (d) it doesn’t rely on the transfer function. The effectiveness of the proposed method is experimentally assessed on a stiffened woven composite plate. The results show that this method can estimate the impact position reliably with acceptable accuracy for engineering applications.
Radar-Based Structural Monitoring of Wind Turbines Blades: Field Results from Two Operational Wind Turbines
Wind energy plays a prominent role in Germany to achieve the climate targets. According to the ”Wind-an-Land-Gesetz” (literally: ”Wind on Land Act”), which has just come into force, the area used for onshore wind energy in Germany should be more than doubled from the current 0.8% to 2% until 2032. Rotor blades are one of the most significant cost factors of a wind turbine. They contribute with about 30% to the plant costs. By means of continuous monitoring of their structural integrity, it would be possible to reduce downtime and maintenance costs. This approach of “structural health monitoring” (SHM) with FMCW-radar systems has already been tested in the laboratory. In this contribution, we describe and demonstrate an SHM system for radar-based monitoring of rotor blades at two operational wind turbines. For this purpose, a sensor box with a 35 GHz radar sensor (1 000 measurements per second) and a camera system (100 images per second), is mounted on each wind turbine tower at approximately 100 m height. In order to distinguish individual rotor blades, a machine-readable marker printed on a self-adhesive film was applied on the blade’s surface. When a rotor blade passes the sensor, the camera captures an image of the marker while the radar records a measurement. The marker is then identified and the recorded data is assigned to a particular rotor blade. The ultimate goal is to detect damage-induced changes in the radar characteristic of the blades. By end of April, over 260 000 rotor blade passes had already been recorded. The data set will be discussed in the paper. Images from the FMCW-radar are classified by the rotor blade label using a convolutional neural network (CNN). Early test results for a subset already show an f1-score of 0.886 and 0.923 for each rotor blade of the evaluated wind turbine.
Separation of Physical from Mathematical Poles in Operational Modal Analysis Using Reduced, Reassembled Covariance Matrices
Modal analysis is one of the elementary tools for the analysis of structural dynamics. In context of vibration-based damage diagnosis and continuous monitoring of modal quantities, different variants of Operational Modal Analysis (OMA) were developed. In most cases, the results of the analysis (modal data) are improved by post-processing methods, e.g., by clustering of stable poles in stability diagrams. The aim of this paper is to facilitate the automatic interpretability of the computational results by preprocessing techniques. The method presented in this paper is used to distinguish between the physical poles and the unwanted mathematical poles to determine the natural frequencies more accurately. This is necessary because for the monitoring of complex real structures, high model orders are required, leading to the occurrence of mathematical poles. To enable a reliable separation, the spectral signal components are analyzed regarding their information content and their energy level in a specific frequency band. For this purpose, the widespread assumption is used that the largest singular values of the system response are related to the highest signal energy and that the lower singular values are caused by noise. With the use of different narrow frequency bands (e.g., by using bandpass filters), the estimation of an accurate threshold between noise and eigenfrequencies can be well established for each band. This technique is applied on the Covariance-Driven Stochastic Subspace Identification algorithm (SSI-COV) for OMA. In context of SSI-COV the Singular Value Decomposition (SVD) and the estimated threshold is used to build reduced and reassembled Hankel matrices from partial sum matrices for each frequency band separately. The advantage of the reduced matrices is that only physical poles have plausible modal damping values, and these can be well separate from mathematical poles. The effectiveness of the method is first demonstrated on simulated data and then successfully tested on a laboratory structure. The results, the advantages, and limitations of the method as well as the need for further improvements are discussed.
Tower Vibration-Based Icing Detection on Operational Wind Turbines
Ice accretion on wind turbines poses a significant safety risk, especially in densely populated areas. Ice built up on the blades can be thrown towards people and infrastructure and cause potential damage and harm. This risk is known and, as such, several solutions exist in today’s market to detect ice accretion on the blades and timely shut down the system. The industry consensus is that icing detection systems installed directly on the blades offer more reliable detection. However, (post-) installation of hardware on the blades poses some practical limitations, impacting the overall cost and maintainability. To complement existing technology this study proposes monitoring the whirling modes, which are the natural frequencies associated with the wind turbine rotor, through a sensitive accelerometer mounted on the tower. The working principles rely on the concept that the increase of mass due to ice will lower the natural frequencies of the rotor. Observing this phenomenon from the tower implies a similar working principle as some blade mounted systems, while having a sensor installed on the tower simplifies installation and maintenance. Utilizing SHM data from tower, an ice indicator was made during standstill and operation which provided a proof of concept for this method.
Fleetwide Interface Fatigue Load Prediction of an Operational Offshore Wind Farm Using a Single Accelerometer and SCADA Data
The operational life of offshore wind turbines is in part driven by the fatigue life of key structural components, such as the substructure. In recent years, fatigue life management of operational assets has become evermore important, as older farms are closing on their design lifetime and newer farms are designed with tighter margins. To support decisions on fatigue lifetime, it is advantageous to monitor the fatigue progression in these structures through SHM. However, a full instrumentation of every asset in a farm to assess the fatigue life of the substructure is considered economically infeasible. The drive for SHM in wind has been accompanied by the increased availability of intelligent data-driven methodologies which have attempted to provide the same information without the need for additional hardware. One such cost-effective approach, as described in [1], uses the available supervisory control and data acquisition (SCADA) systems, coupled with acceleration measurements to predict the fatigue life of an offshore wind turbine. The use of acceleration measurement data has been proven critical for capturing the complex dynamics of offshore wind turbines. In this contribution, we present the results of said data-driven approach using SCADA and Internet of Things (IoT) accelerometer installed at nacelle-level to monitor the fatigue life for the entirety of a real-world offshore wind farm comprised of 23 turbines, with a specific focus on long-term damage equivalent fatigue loads (DEL) estimation [2]. The availability of acceleration measurements for all locations in particular, is fundamental, as to cover all possible differences in natural frequencies between turbines would be nearly impossible if solely relying on wave and tidal data. To achieve this goal, a neural network architecture is used, enhanced by physics-informed learning focused on long-term estimation, trained and validated on so-called fleet-leader (three turbines instrumented with strain gauges, which provide the ground truth). In this study, we pay special attention to the farm-wide validation, cross-validation and extrapolation of these models as well as the performance for different operational conditions. Finally, this study is undertook for a real-world instrumentation setup, unparalleled in its scale, and can thus be indicative of future trends for SHM in offshore wind: farm-wide instrumentation and monitoring of structural health based on acceleration measurements, enabling a greater trustworthiness on reliability and durability estimation over the serviceable lifetime.
A Concept of Radial Pseudo-Force for Damage Detection in Wind Turbine Towers
Damage in a plane structure can cause perturbation to the equation of out-of-plane motion, which can be equivalently regarded as the transverse pseudo-force (TPF) applied to the plane structure. Thereby, the detection of damage in the plane structure can be achieved by identifying the TPF. Nevertheless, it is difficult to formulate the pseudo-force in a cylinder structure such as a wind turbine tower because its radial, longitudinal, and circumferential motions are coupled. The aforementioned limitation leads to a noticeable barrier to extending the TPF to a cylinder structure. To overcome this barrier, a new concept of radial pseudo-force (RPF) is formulated to represent the damage-caused perturbation to the radial equilibrium of a cylinder structure, whose motions are dominated by radial components. The RPF is applied on the lateral surface of the cylinder and is an ideal indicator for the detection of damage in the cylinder structure because it appears in the damage region only and almost vanishes in intact locations. The concept of the RPF is experimentally validated on a wind turbine tower model, on the internal surface of which a pit was manufactured. The results reveal that the RPF-based approach is capable of detecting internal damage in cylinder structures such as wind turbine towers by graphically characterizing the occurrence, location, and size of the damage.
Development and Installation of a Robust and Reliable Research Structural Health Monitoring System for Grouted Joints of Offshore Wind Turbines
Offshore wind turbines are highly exposed to operating loads and extreme environmental conditions. To ensure technically reliable and economically worthwhile operation, high plant availability must be provided. Structural health monitoring (SHM) respectively condition monitoring (CM) makes a valuable contribution to this. The grout connection used in offshore foundations is usually monitored in compliance with regulatory requirements, e. g. VDI 4551 or insurance conditions. In the project "In-Situ-WIND" an extended hard, - and software concept adapted to the SHM-application, based on vibration-based methods, acoustic emission analysis and radar measurement technology was developed. The overall objective of the project is to find a suitable system configuration and appropriate methods to monitor the grout connection. The challenges are that the measurement system needs to operate reliable under extreme environmental conditions in the grout area, must be accessible remotely due to extremely limited accessibility and the offshore installation should be carried out timeefficiently under very difficult installation conditions. Due to this, a decentralized measurement system has been set up. A central server cabinet in the entrance area of the wind turbine contains the essential components for power supply, data storage and data processing. A redundant, diagnosable, and remotely maintainable design of many of these central components increases the reliability of the overall measuring system. One measurement system is placed directly in the grout area in a rugged offshore-ready control cabinet and receives signals from 50 channels of different sensors. Due to limited installation options, a hybrid cable for power supply and data transmission was used to connect the system with the control cabinet in the entrance area. Another measuring system records the vibrations of the tower by several acceleration sensors.
Dimensionality Reduction of Active Vibration Data for Detection and Monitoring of Progressive Damage in Wind Turbine Blade
The present work considers the use of an active vibration monitoring system for wind turbine blade SHM. A test campaign is conducted with a 52-meter commercial wind turbine blade, investigating the potential for detection and monitoring of damage using the active vibration monitoring system. A crack is manually induced in the shear web laminate of the wind turbine blade, and progressive damage propagation is obtained during fatigue testing.
Vibration Transmissibility as a Method of Damage Detection on Horizontal Axis Wind Turbine Blades
Structural health monitoring of horizontal axis wind turbine blades is challenging due to their large size and limited in-service accessibility. Given the harsh environment that wind turbines operate in, the blades are prone to damage throughout their working life. This paper examines the transmissibility of vibration response among embedded sensors over time as a method of damage characterization. After damage occurs, the sensors closest to the new defect will experience the highest change in transmissibility compared to baseline readings. The defect can therefore be identified and located with this method. When assessed at the natural frequencies of the blade, the response transmissibility between two sensors is independent of applied force and magnitude. This makes vibration transmissibility an ideal method for inspecting wind turbine blades that are subject to varying wind speed and direction. A blade was designed based on the NREL IEA Wind -15MW offshore reference wind turbine, with modifications made to fabricate a scaled 3D printed model for testing. The blade was outfitted with MEMs accelerometers to measure acceleration and fiber Bragg gratings (FBGs) to measure strain. To test the transmissibility concept prior to experimental testing, a finite element model was developed to simulate acceleration and strain transmissibility on a damaged and undamaged blade with random force inputs. This model was able to demonstrate that a 5mm transverse crack was visible across the blade when examined at the natural frequencies.
Joint Input and State Estimation of a Scaled Wind Turbine Model: An Experimental Study
Structural health monitoring (SHM) has gained recognition in civil engineering and is now a well-established technique. Recently, it has received considerable research attention in the application in Wind turbine (WT) structures. A complete sensing system could provide comprehensive information about the structure during long-term operations. In this study, a 1:50 scaled WT model of the 5-MW reference WT presented by the National Renewable Energy Laboratory (NREL) was designed and manufactured, and a comprehensive SHM sensing system was installed on the WT tower. The system included accelerometers, strain gauges, and as well as a camera system that integrated digital image correlation (DIC) technology and binocular stereo vision technology. By employing the joint input and state estimation (JISE) technique, both the unmonitored structural responses and the unknown interface input were estimated. The study found that blade rotation exerted significant effects on the WT tower responses. The feasibility of WT SHM using the camera system was also validated and discussed in the research. These findings establish a basis for future dynamic analysis of WT structures using the estimated responses and input. Moreover, the study results present a cost-effective approach for monitoring the structural health of WT systems.
Fatigue Evaluation of Offshore Wind Turbines Using In-Situ Strain Data
The development of offshore wind power has increased rapidly in recent years due to its clean and renewable nature. However, the design of offshore wind turbines is difficult and costly. As a result, the development cost remains high. Among them, fatigue of the steel structures in offshore wind turbine foundations is one of the most severe structural diseases, significantly affecting offshore wind development and maintenance costs. This paper proposes a feasible approach to study the fatigue damage of offshore wind turbines using the in-situ strain data based on a statistical analysis of stress amplitude and cycles. First, the hot-spot stress at the corresponding monitoring locations is calculated using the strain monitoring data from an offshore wind farm in the East China Sea. Second, the stress amplitude and cycles are solved using the rain-flow counting method. Subsequently, the weekly distribution statistics of stress amplitude and cycles are fitted using Weibull distribution. The Chi-square test is used to verify the confidence level of the probability density function of the weekly stress amplitude and cycles. Finally, according to the statistical distribution of stress amplitude and cycles, the cumulative damage is analyzed based on the hot-spot stress S-N curve of steel structures with cathodic protection in seawater. The fatigue damage obtained based on the monitored data is roughly consistent with the numerical calculation results. However, the proposed method underestimated the fatigue damage compared to the design results obtained by numerical prediction. Though these fatigue results are not mutually consistent, they can all confirm that the wind turbine would not suffer fatigue issues at present, as the fatigue damages are all below the threshold. Further efforts should be made to handle this inconsistency in fatigue prediction.
Quantifiable Scour Detection for Offshore Wind Turbines Using Resonance Frequency Monitoring and a Digital Twin
This paper presents a methodology for enhancing frequency-based Structural Health Monitoring (SHM) of Offshore Wind Turbines (OWTs) by incorporating digital twin models. The proposed methodology utilizes an automated operational modal analysis to extract modal frequencies from acceleration measurements. The environmental and operational variability is removed through tree-based regression models, which can adequately recognize the different operational states of the OWT. The proposed data-driven approach relies on the detection of the modal frequency changes caused by a damaging scenario in the OWT (e.g. scouring). The digital twin model accounts for the actual properties of the substructure, considering the specific foundation geometry and soil conditions at the monitored OWT location. A statistical control chart is used to identify the possible scour depth by comparing the changes in the normalized modal frequency and the digital-twin calculations. The methodology is demonstrated on 4 months of measurements from a full-scale monitored OWT. The proposed methodology advances the SHM capabilities from damage detection to damage quantification, enabling improved operations & maintenance strategies for OWTs.
Developing Digital Twins of Dynamic Systems Using Vision Techniques, Multi-View Stitching, and Expansion Methods
This study aims to create digital twins of structures such as wind turbines using noncontact techniques integrated with numerical methods. As part of this study, a stereo camera system mounted on a semi-autonomous drone captures the dynamic properties of a wind turbine. The drone with digital image correlation (DIC) cameras scans the entire structure to capture the deformation data of each field of view. The measured data includes the geometry, and displacement field data is mapped into a global coordinate system using 3D transformation matrices. The obtained data in the time domain for each field of view is transformed into the frequency domain to extract the operational deflection shapes and resonant frequencies for each field of view. The obtained deflection shapes are scaled and stitched in the frequency domain to extract the operating deflection shapes of the entire turbine. The operating data for the wind turbine is expanded using modal transformation techniques. A numerical model of the turbine is updated using expanded modal data. This methodology can be applied to any mechanical system subjected to dynamic loads. The work provides engineers with a new procedure to create and update digital twins using a limited set of measured data captured with non-contact techniques. The proposed approach to creating digital twins of structures can be used for full-field structural dynamic predictions, durability analysis, and structural health monitoring.
Automated Operational Modal Analysis for the Monitoring of a Wind Turbine Blade
Modal analysis has developed into a major technology for the study of structural dynamics in the past several decades. Through it, complex structural dynamics phenomena can be represented in terms of structural invariants, i.e., the modal parameters: natural frequencies, damping ratios and mode shapes. Operational Modal Analysis (OMA) deals with the estimation of modal parameters on vibration data measured for operational conditions, when the excitation on the structure is not measured. In this work, OMA is performed on a wind turbine blade undergoing wind tunnel testing. These tests included different wind speeds, pitch angles and also different health conditions of the blade, where masses of different magnitude were fixed to the blade on different locations to emulate damage conditions. In order to monitor the modal parameters across multiple days of varied tests in the wind tunnel, the Polymax modal parameter estimator was implemented, coupled with an Automated Modal Analysis methodology. This methodology included an automatic modal parameter selection technique, using a Machine Learning (ML) clustering algorithm, coupled with a modal tracking procedure which applied statistical thresholds on the modal parameters’ values. The tracking procedure searches for modes similar to the ones calculated for healthy conditions. The results show how the modal parameters of the wind turbine blade vary with the different measured conditions in the wind tunnel. Moreover, a damage detection methodology is implemented to differentiate between the healthy and damaged conditions on the blade, by leveraging an anomaly detection algorithm using the Multivariate Gaussian Distribution (MGD). This algorithm takes as input the modal parameters calculated by the previous Automated Modal Analysis methodology and detects statistical deviations among them which could indicate the presence of damage. All steps of this work contribute to developing an automatic framework able to detect damages on a wind turbine blade, and therefore perform Structural Health Monitoring (SHM) for different operational conditions.
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a hierarchical Bayesian model to infer expected soil stiffness distributions at both population and local levels, as a basis to perform anomaly detection, in the form of scour, for new and existing turbines. To do this, observations of natural frequency will be generated as though they are from a small population of wind turbines. Differences between individual observations will be introduced by postulating distributions over the soil stiffness and measurement noise, as well as reducing soil depth (to represent scour), in the case of anomaly detection.
Wind Turbine Monitoring Using Optical Motion Magnification: Challenges and Opportunities
An inexpensive, quick, and robust structural health monitoring technique to assess the integrity of wind turbine foundations and components remains elusive. Traditional techniques for wind turbine condition monitoring (CM) are costly, invasive, and cause downtime in the asset. As turbines age, wear-induced damage may accumulate in the structural and mechanical components. The ability to effectively monitor wind turbine foundations and blades using optical methods is explored in this work. Specifically, optical motion magnification (OMM) is investigated for this application. OMM is a computer vision-based monitoring technique that can extract and amplify unperceivable displacements in a video of a structure of interest. These magnified videos effectively reveal the areas in a system with the largest displacements. OMM is a remarkable visualization tool, giving technicians the ability to quickly identify areas with problematic vibrations in an easily understood manner. OMM also generates quantitative displacement time histories from which the amplitude and frequency spectra of the structure can be extracted. This work describes two experimental studies completed to determine the feasibility of OMM when this approach is used to inspect wind turbine foundations and blades. Firstly, an experiment exploring the use of OMM for modal analysis of turbine blades was performed, comparing the frequency spectra calculated using both optical and traditional contact-based measurements. OMM proved capable of identifying resonant frequencies with an error less than 2.5% when compared to conventional measurements. Next, proof-of-concept turbine foundation tests were completed to ascertain the viability of the method to distinguish between questionable and healthy foundations. The results of this research show how OMM can potentially eliminate several issues inherent in standard testing and monitoring procedures, namely reducing the overall cost of the sensor systems and time spent collecting data. If further developed, OMM can open the doors to many more innovative solutions, applications, and optimizations of current CM practices for large-scale engineering systems.
Effect of Space Environment on Structural Diagnostics
Space structures such as rockets, spaceships, and satellites are subject to environmental factors depending on their mission. As mankind becomes more dependent on various space assets, understanding the behavior of structural components in space grows in importance. For commercial space transportation, three environments are of the most interest: suborbital, orbital and interplanetary. Currently, commercialization priorities are focused on suborbital and orbital applications. This contribution explores potential effects of suborbital and orbital environments on structural diagnostics of spaceships. The primary concern for suborbital flight is survivability of structure during launch and re-entry. This is when most of the dynamic loads occur which may cause mechanical failure of the spacecraft. In addition, the suborbital flight is characterized by high thermal loads occurring during re-entry. Given that during suborbital flight, spaceship spends only minutes in space, the contribution of actual space environment to such flight is minor with rather low radiation doses and stable temperature range. The environmental contribution changes when a structure is placed on low earth orbit (LEO). The temperature could vary between -120 oC to +120 oC in this orbit and this thermal variation could cause thermal fatigue on structures leading to formation of cracks. Absence of atmosphere (pressure in the order of a few micropascals) affects a vibration environment. Atomic oxygen (AO) considerably affects non-metallic materials, causing their deterioration on LEO. Materials on LEO are subject to UV, particulate and ionizing radiation with each of them being responsible for different deterioration mechanisms. Micrometeorites with speeds exceeding several km/s could cause notable damage. In this contribution, we study effect of space environment on piezoelectric-based SHM. Thermal effects are considered first, which are followed by the radiation environment. Results of laboratory experiments are presented along with the theoretical developments. Recommendations are suggested for utilization of SHM on LEO.
Durability of Surface Mounted PZT and FBG Guided Wave Sensors Under Reusable Launch Vehicle Representative Thermal Cycling
This paper addresses the durability of cobonded PZT and FBG sensors for hybrid Guided Waves (GW) SHM systems under thermal cycles partially representative of a Reusable Launch Vehicle (RLV) use case. The cobonding process is tested as a potential alternative to the secondary bonding process for attachment of SHM sensor network onto composite structures. The evolution of the sensors with the thermal cycles is continuously monitored through measurement of the electromechanical impedance (EMI) spectrums (for PZT) and reflection spectrums (for FBG). Then the effects on the PZT sensors performances are investigated by measuring GW pitch-catch signals at different stages of the thermal cycles. As for FBG sensors, the thermal cycling is still ongoing and is expected to be completed by the time of the conference: additional results will be discussed then.
A Structural Health Monitoring System for Impact and Damage Detection in Space Structures
Space-based assets, such as satellites, are highly vulnerable to damage at all stages of their lifecycles. In particular, on-orbit events, such as impacts with space debris, micrometeoroids, and other vehicles, present major operational and logistical concerns. A Structural Health Monitoring (SHM) system deployed on satellite structures holds the promise of in-situ detection of impact events and monitoring of associated damage, thereby becoming a critical component of any On-orbit Servicing, Assembly, and Manufacturing (OSAM) strategy. The authors explored the feasibility of such an SHM system by instrumenting aluminum plates and a satellite panel with an integrated network of distributed piezoelectric sensors and then subjecting them to a series of impact tests with a wide range of forces. Satellite panels typically have a complex geometry due to stiffening ribs, and, consequently, variable stiffness. Therefore, signals obtained from the sensors were processed taking into consideration the geometry of the impacted areas, which allowed the location of the impact on the satellite panel to be accurately determined. The impact locations and force magnitudes estimated by the SHM system were compared with the actual values and found to have a high degree of correlation, with some avenues for additional improvement identified. In addition, the SHM system’s damage-detection capability was validated in both test structures. The results show that SHM systems can be readily applied to space structures in order to provide both impact- and damage-detection capabilities.
Flexible Multifunctional Structural Health Monitoring Systems for Inflatable Space Habitat Structures
Space structures are one of the most critical components for any spacecraft, as they must provide the maximum amount of livable volume with the minimum amount of mass. Inflatable, deployable structures in particular, have been investigated by NASA since the early 1950’s and used in a number of spaceflight applications. Inflatable habitats, airlocks, and space stations can be used for in-space living spaces and surface exploration missions. Inflatable structures are being pursued as candidates for long-term habitats in space and on the surfaces of the Moon and Mars. Many concepts by the NASA and industry utilize high-strength, low-weight softgoods materials, such as Vectran, as the primary load-bearing structure in inflatable habitats. The ability to monitor and assess the structural health of an inflatable module is an important factor in determining the feasibility of using inflatable technologies for habitat requirements, especially in the presence of micrometeoroid and orbital debris (MMOD) threats. There is therefore a need for Structural Health Monitoring (SHM) methods to perform detection, localization, and quantification of damage to structural layers throughout the structure’s mission. This capability must be accomplished within real constraints for sensor volume, mass, and crew resources, including being able to perform effective damage monitoring of the inflatable habitat layers from the interior during a mission either on a routine basis or as a quickresponse basis. This paper discusses the development of an approach for SHM for inflatable softgoods, and testing a laboratory proof of concept of the preliminary design, which integrates the SHM approach into inflatable habitat test articles. An experimental test setup for performing the initial feasibility demonstration for impact detection using SMART Layer sensors. An initial test setup for testing was developed and testing performed for impact detection.
Satellite InSAR Technology for Displacements Monitoring of Bridges: A Comparison with Onsite Topographic Measurements and Uncertainty Quantification
Satellites equipped with Synthetic Aperture Radar (SAR) sensors can capture millimetre-scale displacement of the Earth's surface and man-made structures regardless of atmospheric conditions or lighting. Over the past two decades, Interferometric SAR (InSAR) has found many applications, including monitoring subsidence, volcanoes, and glaciers. While there have been some applications in civil engineering, a metrological validation of satellite technology for remote structural health monitoring (SHM) compared to traditional on-site SHM is lacking or inconclusive. This research aims to validate the use of satellite based InSAR technology for SHM of road bridges by directly comparing the time series of displacement extracted with InSAR on the Colle Isarco Viaduct, Italy, with those obtained by on-site topographic monitoring. This study also compares the deformed shape of a subpart of the bridge, which is modelled based on satellite and on-site measurements, to verify the possibility of reconstructing the bridge distortion over time based only on satellite data. The goal is to analyse this new technology's accuracy and reliability and identify its strengths and limitations. If deemed reliable, this technology could simultaneously monitor many civil infrastructures at a significantly lower cost than traditional site monitoring systems.
On Engle-Granger Cointegration Using Treed Gaussian Processes
Over the last decade or so, cointegration has emerged as arguably the state-of-the-art in terms of removing Environmental and Operational Variations (EOVs) from structural health monitoring (SHM) data. When data channels share common trends which can be removed by linear projection, the Johansen procedure, a maximum-likelihood approach developed within the field of econometrics, is provably optimal. Unfortunately, SHM problems can present where the trends occupy a nonlinear submanifold of the feature space, and in this case, linear cointegration/projection fails. It is still possible to make progress in this case by moving to the older Engle-Granger approach to cointegration, where one linearly regresses one of the feature space variables on the others; nonlinear cointegration is then ‘simply’ the application of an appropriate nonlinear regressor. Over the years, a number of nonlinear regression algorithms have been applied, motivated by machine learning or evolutionary computation; each with pros and cons. The aim of the current paper is to demonstrate an approach based on Treed Gaussian Processes (TGP); the advantage being that the algorithm allows switching between cointegration models in different parts of the feature space. Examination of the switching points can provide insight into the physical processes driving the nonlinearity. The approach is demonstrated here on the well-known Z24 Bridge data set, where the ambient temperature drives EOVs which cannot be removed by linear methods.
A Decision Framework for Selecting Information-Transfer Strategies in Population-Based SHM
Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems. Unfortunately, the limited availability of labelled training data hinders the development of the statistical models on which these decision-support systems rely. Population-based SHM seeks to mitigate the impact of data scarcity by using transfer learning techniques to share information between individual structures within a population. The current paper proposes a decision framework for selecting transfer strategies based upon a novel concept – the expected value of information transfer – such that negative transfer is avoided. By avoiding negative transfer, and by optimising information transfer strategies using the transfer-decision framework, one can reduce the costs associated with operating and maintaining structures, and improve safety.
Enhancing Structural Health Monitoring with Machine Learning and Data Surrogates: A TCA-Based Approach for Damage Detection and Localisation
Structural health monitoring (SHM) involves constantly monitoring the condition of structures to detect any damage or deterioration that might develop over time. Machine learning methods have been successfully used in SHM, however, their effectiveness is often limited by the availability of data for various damage cases. Such data can be especially hard to obtain from high-value structures. In this paper, transfer component analysis (TCA) with domain adaptation is utilised in conjunction with high-fidelity numerical models to generate surrogates for damage identification without the requirement for high volumes of data from various damaged states of the structure. The approach is demonstrated on a laboratory structure, a nonlinear Brake-Reuß beam, where damage scenarios correspond to different torque settings on a lap joint. It is shown that, in a three-class scenario, machine learning algorithms can be trained using numerical data and tested successfully on experimental data.
On the Use of Model-Based Versus Data-Based Approaches for Virtual Sensing in SHM
Structural Health Monitoring (SHM) typically aims at producing methodologies for the periodic, and often online, assessment of structures. While there are different approaches to SHM, there is one common necessary element if applied in practice - data acquired from sensors. In an ideal scenario, sensors would be deployed anywhere on a structure of interest and could be added retrospectively, if not included in the original design. In practice, there are various limitations to the availability and installation of sensors, such as physical access and cost. Virtual sensing has thus been proposed as a solution to the problem of sensor availability and different approaches have been applied in various fields. This paper applies and compares two approaches for virtual sensing in structural dynamics: modal expansion which is model-based and Gaussian Process (GP) regression which is data-based. The approaches are demonstrated on data from a Piper PA-28 tailplane structure, which was tested with the help of a scanning-laser vibrometer under laboratory conditions.
Large-Span Bridge Strain Reconstruction Based on Bidirectional LSTM and ESN
Partly missing and anomalous of the data collected from structural health monitoring (SHM) systems are inevitable due to the failure of sensor and data acquisition equipment, which lead to misjudgment of the target structure state. The data integrity demands for guaranty using reconstruction algorithms before signal processing. Recurrent neural networks (RNN) has been proved effective of reconstruction issue by learning from the historical and future signal segments. The gated RNN represented by long short-term memory (LSTM) networks and reservoir computing represented by echo state networks (ESNs) show superiority on accuracy or training efficiency than standard RNN method. In addition, bidirectional concept can be introduced into these two methods to further improve their reconstruction precision. In this paper, models built by bidirectional LSTM and ESN are used to reconstruct strain data measured by the SHM system of Tsing Kau bridge, during which performances in both time and frequency domains are compared and evaluated. Furthermore, hyperparameters including number of layers, number of hidden units, scale of reservoir, and leaky rate have been optimized to improve the structures of the proposed models.
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
At present, most surface-quality prediction methods can only perform single-task prediction [1] which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared to multiple independent Bayesian linear regression models to showcase the benefits of partial pooling in a machining setting with respect to prediction accuracy and uncertainty quantification.
An Ensemble Learning-Based Alert Trigger System for Predictive Maintenance of Assets with In-Situ Sensors
Failure of assets, such as machines, engines, or equipment, can cause significant loss to an enterprise. Real-time monitoring of the asset operation and early detection of failure could guide mechanics or engineers to check the assets in time and reduce the chance of a breakdown. With the availability of Internet of Things (IoT) technologies and the development of machine learning, predictive maintenance has become an effective way to monitor asset performance and detect anomalies using sensor data and historical information. However, many use cases need more information to evaluate the results. Lack of validation can undermine confidence in predictive results. To solve this problem, a novel alert trigger schema that integrates sensor fusion, feature extraction, machine learning, ensemble strategy, and an alert format is proposed. The method offers a comprehensive and reliable approach to detecting anomalies based on unlabeled data, utilizing sensor and decision-level fusion techniques. Meanwhile, eight anomaly detection techniques were investigated, including K-Means Clustering, Gaussian Mixture Model, Autoencoder, and Isolation Forest. Various algorithms generate results with differing degrees of confidence. These results are consolidated into a single indicator representing the alert level. This amalgamation of data ensures the provision of robust and reliable predictions. Instead of simply combing the alert information, confidence in different algorithms is reflected in adding different weights in the ensemble process. In addition, while other existing frameworks focus on evaluating the algorithm’s accuracy, more effort was put into demonstrating a levelbased alert system, showing diagnosis changing with time. In that way, mechanics or engineers can get information to check the asset’s status. The proposed framework was validated using a synthetic dataset based on recordings from a rotating fault simulator that generates multi-modal data, including accelerometer, acoustic, and tachometer data, representing the run state of the rotating components. The alert system showed different levels of warning for predictive maintenance. The framework is designed with high flexibility and scalability. Therefore, this framework can be generalized to other in situsensor data from various assets.
Hierarchical Bayesian Modelling of a Family of FRFs
Population-based structural health monitoring (PBSHM) aims to share valuable information among members of a population, such as normal- and damage-condition data, to improve inferences regarding the health states of the members. Even when the population is comprised of nominally-identical structures, benign variations among the members will exist as a result of slight differences in material properties, geometry, boundary conditions, or environmental effects (e.g., temperature changes). These discrepancies can affect modal properties and present as changes in the characteristics of the resonance peaks of the frequency response function (FRF). The hierarchical Bayesian approach provides a useful modelling structure for PBSHM, as population- and domain-level distributions are learnt simultaneously to bolster statistical strength among the parameters, and reduce variance among the parameter estimates. This paper provides an overview of current work, where hierarchical Bayesian models are developed for a small population of nominally-identical helicopter blades, using FRF data. These models account for benign variations that present as differences in the underlying dynamics across the input space, while also considering (and utilising) the similarities among the blades.
Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge
We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commuter train. The measurements are treated as a longitudinal dataset and represented with a (low-rank approximation) hierarchical Gaussian process. For each unit in the combined model, we encode domain expertise as boundary condition constraints and work towards a general representation of the strain response. Looking forward, this should allow for the simulation of train types that were previously unobserved in the training data. For example, trains with more passengers or freights with a heavier payload. The strain event simulations are valuable since they can inform further experiments (including FEM calibration, fatigue analysis, or design) to test the bridge in hypothesised scenarios.
Predicting Axial Stress in Continuous Welded Rails Using Machine Learning
Buckling is a major contributor to derailments on continuous welded rails (CWRs) and is caused by extreme compression due to high temperatures. Thus, the determination of the axial stress is necessary to gauge when buckling may occur. As many techniques are often invasive, this study focuses on the latest advancements of an ongoing noninvasive technique based on the use of low-frequency vibrations using machine learning (ML). Based on the theory seen in guitar strings, the relationship between the frequency of vibration and tension/compression is used to associate spectral features to axial stress. This in turn enables the determination of the rail neutral temperature (RNT), which is the point at which the net longitudinal force is zero. As the RNT is not only a function of the stress but also varying boundary conditions associated with the ballast, fasteners, and ties, the RNT can change frequently over time. This stresses the need for a system that is also flexible to this variability. In order to capture some of this, field data was captured twice in Pueblo Colorado using several accelerometers where vibrations were induced with an instrumented hammer. Both a wood-tie and concrete-tie rail section were experimented on with the latter having a 5o curve. The accelerometers consisted of both wired and wireless sensors, with the wireless counterpart being used to prove the replacement of the wired in the second field test. Using the power spectral densities (PSDs) associated with the lateral and vertical direction as input to an artificial neural network (ANN), the RNTs were predicted and compared to those determined by an independent third party. Our predictions showed very good agreement with the RNT captured by conventional strain-gage rosettes. In this paper, an analysis of our two field tests as well as the impact of boundary conditions data on ML predictions is explored.
Enhancement of Structural Health Monitoring Framework on Beams based on k-Nearest Neighbor Algorithm
The aiming of this work is to enhancement the structural health monitoring (SHM) framework of beams structure for damage detection to treatment the drawbacks of poor detection efficiency in traditional of beams monitoring algorithms, the improvement framework on beams SHM is based on novel data classification technique through designing the k-Nearest Neighbor (k-NN) algorithm. First, the beam finite element model under impact load is analysis, and the cumulative damages are considered and introduced to beam model. The datasets of beam SHM are compiled from the sensors installed in beam structure, and then processed using kernel principal component analysis to remove the unnecessary features and reduce the scale of classification features. The k-NN algorithm parameters of beam SHM are determined by the genetic optimization algorithm (GOA) to establish the optimal SHM classification model of beam. Finally, a comparison between the present damage detection results via k-NN and traditional models via convolutional neural network (CNN) and supper vector machine (SVM) results available in literatures is established through the most significant indexes of testing to check the effectiveness and superiority of suggested method. The results show that the presented SHM model are gave higher precision, reduced the time of modeling, and improvement the total performance of damage detection model in beams. The current performance are recorded 95.3%, 91.8%, and 89.7%, for accuracy rate, recall rate, and F-score respectively.
Incorporating Modal Testing into Dynamic Load Identification from Structural Vibration Measurement
Load identification from vibration measurements is usually cost-effective for obtaining dynamic loads on structures. Modal testing enables structural models to be validated or updated prior to being used for load identification. This paper proposes a methodology to make better use of modal test data for developing load identification methods. Modal test data are split into two datasets. One is used for model updating, and the other is used to validate load identification methods by inputting the measured vibration response and comparing the identified loads with the measured loads. Crossvalidation can be achieved by using different splitting of data. This paper demonstrates this methodology with a case study in which impact hammer tests are performed on a stiffly-connected assembly. The test data support the optimization-based updating of a simplified model and the validation of a proposed Newmark-β-based load identification method. The results show that different impact locations of the model updating dataset affect the identification accuracy of the load identification dataset. The Newmark-β- based method yields comparable performance to the traditional frequency-domain method, while it has the advantage of being implemented at each time step.
Research on Modal Identification of High- Speed Maglev Guideway Structure Based on Data Fusion and Genetic Algorithm
High-speed maglev guideway structures are an important part of high-speed maglev transportation systems. A precise and efficient modal identification method for guideway structures is proposed to study the dynamic characteristics of highspeed maglev systems. Ensuring the safety of high-speed maglev transportation systems is of great significance. Taking the high-speed maglev guideway structure as the research object, this paper proposes a method for optimizing the sensor arrangement based on a genetic algorithm and a method for identifying the multiorder modes of the guideway structure based on data fusion. By optimizing the arrangement of the piezoelectric acceleration sensors and fiber Bragg grating sensors, data on the various dynamic characteristics of the high-speed maglev guideway structure under the excitation of high-speed maglev trains were obtained. The multimode characteristics of the high-speed maglev guideway structure were identified using the natural excitation technique and eigensystem realization algorithm through the fusion of multisource data of different types of dynamic characteristics in this study. The effectiveness of the proposed method is demonstrated by comparing the results of modal identification in different situations, for instance fusion of multi-source data. The research results indicate that the optimization of the arrangement scheme of sensors of high-speed maglev guideway structures based on genetic algorithm can effectively reduce the number of sensors on the premise of ensuring the accuracy of modal identification results, and multisource data fusion can significantly improve the accuracy of modal identification of the guideway structure, which proves the efficiency and accuracy of the methods proposed in this paper. The research results can provide effective methods for optimizing the sensor arrangement scheme for testing the dynamic characteristics and analyzing the dynamic characteristics of high-speed maglev guideway structures.
Rail Roughness Identification Via On-Board Acceleration Data and Bayesian Filtering
The global demand for transportation has resulted in extensive expansion of railway networks. However, ensuring the safety, reliability, and efficiency of these rapidly expanding railway infrastructures requires monitoring of their structural health. Focusing on tracks, traditional visual inspections and portable measuring devices are commonly used to gather geometric data for diagnosing and predicting track defects. In recent years, railway operators worldwide have employed specialized diagnostic vehicles equipped with optical and inertial sensors to collect track data and assess its condition. This approach has revolutionized rail condition assessment by introducing a mobile data acquisition platform for track inspection. Nevertheless, deploying these specialized vehicles disrupts regular rail service, limiting their frequency of operation and the continuous collection of rail data. To address this limitation, this study explores an on-board monitoring (OBM) method that focuses on collecting vibration data from traveling trains. The proposed methodology involves gathering acceleration data from axle boxes of trains running at normal speeds. What sets this approach apart is its use of realistic train models and the consideration of the dynamic interaction between the trains and tracks, which is typically oversimplified. The train model employed is simplified to reduce computational requirements. The identification process relies on sequential Bayesian inference for joint input and state estimation. By estimating the input, the relevant rail roughness profile can be identified, thereby providing information on the presence of isolated defects, such as welded joints and squats, along the track system.
Interaction between Mass and Stiffness Parameters of Connections for Structural Parameter Estimation of a Steel Grid
In any structural system, the mechanical properties of its connections impact the structure’s global performance. Particularly, the rigidity of joints plays a substantial role in the system’s vibration response. Since connections typically include several components with complex geometry, they are usually oversimplified in the modeling process. Therefore, the analytical model is typically a low-fidelity representation of the connections’ performance. Although simplifying joints for modeling purposes is a good practice for engineering applications, it may reduce the accuracy of the model predictions. Thus, it is essential to reconcile accuracy with efficiency, particularly for complex joint modeling. In addition to the rigidity, the mass of connections may be another influential structural parameter for the system’s global response. The joint mass may be underestimated in the modeling procedure because the elements of joints in the steel connection regions, including plates, stiffeners, and bolts, are not often modeled in simplified analyses. Hence, as the analytical model is updated, connections’ mass and stiffness need to be estimated and incorporated into the updated model. This paper studies the interaction of mass and stiffness estimation for bolted connections of an experimental steel grid. The structure used in this research is a well-known structure built, instrumented, and tested at the University of Central Florida. The laboratory setup simulates the behavior of deck-on-beam bridges. In its initial analytical model, all grid joints are assumed to be completely fixed, and no extra mass is considered in the joint regions. The model updating procedure is limited to estimating joints’ mass and stiffness values. The modal information of the structure is extracted from the measured frequency response functions of the grid excited in a vertical hammer impact test. Stiffness- and flexibility-based error functions are employed for the model updating. The results demonstrate how the selection of joints’ uncertain parameters can affect the procedure of finding suitable structural layouts for the semi-rigid connections of the grid. The model updating procedure may be generalized to condition assessment and structural health monitoring of bridges, especially when their connections are subject to damage and failure.
Uncertainty Laws of MIMO Modal Identification
‘Uncertainty law’ aims at closed-form asymptotic formulas for the relationship between the identification uncertainties of modal properties (e.g., natural frequency, damping ratio) and test configuration (e.g., number and location of shakers/sensors, noise level, data duration). Focusing on the case of experimental modal analysis with multiple-input and multiple-output, the uncertainty laws of modal parameters for wellseparated modes are proposed in the paper. Asymptotic expressions for the posterior coefficient of variation of modal parameters are derived via the Fisher information matrix for long data and small damping scenarios. Assumptions and theory are validated using synthetic test data. Governing factors motivated by the theory are investigated, including the equivalent modal signal-to-noise ratio and data duration. The developed uncertainty laws provide a scientific basis for planning and managing identification uncertainties in vibration tests with known multiple-inputs
Continuous-Time State-Space Neural Network and Its Application in Modeling of Forced-Vibration Systems
Rapid advances in machine learning make it possible to formulate surrogate models for complex forced-vibration systems using neural networks. Recently, the continuoustime state-space neural network (CSNN) has shown great potential and has been drawing growing attention from the community. In this paper, we propose a generalized CSNN model for various forced-vibration systems. The CSNN model comprises two sets of independent neural networks aimed to compute the state derivative and system response, respectively. Both neural networks adopt linear and nonlinear layers in parallel, aimed to enhance the CSNN model with the capability to recognize the linear and nonlinear behaviors of systems. Additionally, the bias options in the CSNN model are all turned off to improve the stability of the model in the long-term time-series forecast. Integration on the state derivative is executed using the explicit 4th-order Runge-Kutta method. An illustrative example is provided in this paper, demonstrating that the CSNN model can achieve high performance and training efficiency with a few hyper-parameters.
System Identification Via Auto-Encoders: A Comparison Between Data-Driven and Physics-Informed Solutions
Variational Auto-Encoders (VAE) and Long Short-Term Memory (LSTM) are investigated in this article in the framework of Structural Health Monitoring (SHM). The presented approaches aims to combine sensor data with numerical modelling of the dynamical system in a Reduced Order Modelling setting. Two Finite Element case-of-study are proposed with the scope of identify non-linear forces and physical parameter degradation. Starting from a reduced set of sensors data and different levels of knowledge of the physical system, the reconstruction capabilities of time series data are presented and compared for both proposed architectures.
Vertical System Identification of a 52-Story High-Rise Building Using Seismic Accelerations
In this study, various system identification approaches are utilized to estimate the dominant, vertical-component modes of a 52-story, steel, moment-and-braced frame building in downtown Los Angeles resulting from vertical seismic accelerations. Tall buildings exhibit complex threedimensional responses during an earthquake due to the varying material and geometric properties along the building’s height. For high-rise buildings, the dynamic response during shaking events is often sensitive to multiple vibration modes, and multiple-mode structural behavior under horizontal ground motion has been extensively studied. However, the vertical component of ground motion can also excite higher modes and vertical-polarity propagating seismic waves. Their effects are seldom studied due to the scarcity of data. Still, they are important because they can provide information on the axial loads on columns or stresses at floor slab connections. The 52-story high-rise, with its dense triaxial sensor array distributed vertically along the height of the building, provides a suitable basis for examining vertical responses. System identification is performed using state-space methods with low-amplitude earthquake data. Given the high spatial density of the building recordings, we show how we can detect modal characteristics and identify the type of deformation that can occur when considering the vertical component of seismic responses.
Physics-Informed Neural Network for Nonlinear Structural System Identification
Structural system identification is a critical task in resilience assessments, especially following a natural hazard. In this paper, we propose PIDynNet, a novel physicsinformed approach that produces an ordinary differential equation-constrained neural network model for structural system identification. PIDynNet improves the estimation of structural parameters of nonlinear structural systems by embedding an auxiliary physicsbased loss term into the overall loss function as well as a supervised data-driven loss term. The proposed framework has the generalization capability to predict nonlinear structural response given unseen ground excitations. Two nonlinear numerical experiments are conducted to demonstrate the advantage of PIDynNet over other identification methods in problems with or without latent variables.
Improving Hybrid Model Accuracy for Structural Analysis: The Effects of Parameterized Joint Stiffness on System Equivalent Model Mixing
System equivalent model mixing (SEMM) is a hybrid model that integrates experimental data and numerical analysis to obtain an unmeasured degree-of-freedom (DoF). SEMM extends the frequency response functions obtained from experimental data to a finite element method (FEM) model, resulting in a highly accurate and precise hybrid model that enables the measurement of joint behavior and characteristics. By leveraging the precision of experimental data and the expansion of numerical models, SEMM can acquire frequency response functions from inaccessible joints. This approach provides researchers with more complete information on joint behavior and characteristics than traditional modeling approaches that rely solely on either experimental or numerical data, allowing for more accurate and comprehensive analysis. A common method for implementing SEMM involves using a virtual point transformation (VPT) that accounts for the rotational degrees of freedom of the joint by employing three tri-axial sensors near the joint, or by attaching a transmission simulator (TS) to the joint to mitigate the influence of rotational degrees of freedom. However, the accuracy of the expansion DoF using SEMM is determined not only by the initial boundary condition of the analytical model but also by the boundary condition error between the experiment and the analytical model. Therefore, the accurate establishment of system boundary conditions in the numerical model is crucial for SEMM modeling to improve the reliability and correctness of the resulting hybrid model. This paper describes the development of hybrid models for structural analysis by incorporating parameterized joint stiffness to optimize boundary conditions in numerical models and enhance the accuracy of the resulting hybrid models. The accuracy of the proposed approach based on SEMM is evaluated using the frequency response assurance criterion (FRAC). The results show that the FRAC is sensitive to changes in boundary stiffness and that the accuracy of FRFs is directly proportional to the similarity between the numerical and experimental boundaries. To further investigate the impact of the measured DoF, we applied classical sequential sensor placement optimization techniques to select the optimal DoF in the experiment and evaluated their performance accordingly.
Damage Localization in an Aluminum Plate Using Built-In Piezo-electric Sensor Induced Ultrasonic Signals
This paper summarizes a study by students taking the class of AA257 Structural Health Monitoring of Spring of 2023 at Stanford University as a term project to predict the location and size of a single crack in an Aluminum plate using ultrasonic waves induced by surface-mounted piezoelectric discs as sensors or actuators. Three methods were presented for determining cracks in the structure, which include: i) calculating the ratio of the raw signals for different paths, ii) evaluating the ratio of the scattered signals for different paths, and iii) determining the damage index for different paths. Of the three methods presented, the second and the third method using the scatter and the damage index, respectively, are the most accurate in locating the crack.