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A UAV-Based Platform for Real-Time Damage and Defect Identification in Concrete Structures

ELLIOT RANSOM, TANAY TOPAC, RUIQI CHEN, GRAYSON ARMOUR, MICHAEL CHARLES MARSH, JR., KAMYAB ZANDI

Abstract


Digital Twin of an infrastructure is a living digital simulation that brings all the data and models together and updates itself from multiple sources to represent its physical counterpart. The primary focal point of the present study is to propose a framework for Digital Twin of infrastructure and to demonstrate it in the context of a next-generation condition assessment method. The proposed framework is based on the optimized integration of: (1) Structural Inspection: Autonomous Data Collection using drones to minimize intrusion on the transport flow, cover large areas in a minimum of time, access to hard-to-reach areas and minimize exposure to safety hazards for inspectors and users; (2) Damage Quantification: Automated Data Interpretation using data-driven techniques to detect and quantify geometrical and visual anomalies. e.g. cracking and spalling, on the surface and sub-surface of concrete infrastructure; and (3) Performance Prediction: Advanced Structural Simulation combined with physics-based deterioration models to calculate structural performance. The outcome of the study is expected to radically transform the current practices by leveraging drones for inspection, data-driven models for damage quantification, and physics-based models for performance prediction, all seamlessly connected to a living simulation platform “Digital Twin” which updates itself after each inspection round.


DOI
10.12783/shm2019/32289

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