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Developing Integrated Methods and Software Tools for Monitoring-based Asset Performance Management

ZHENG YI WU

Abstract


With the increasing concerns on aging infrastructure and environmental sustainability, structural health monitoring is not only the integral part of intelligent infrastructure, but also plays an essential role for infrastructure operators and owners to achieve cost-effective and carbon-efficient design, construction and operation. The ubiquitous applications of sensing and scanning technologies enable engineers to conduct monitoring-based asset performance assessment and decision-making. It raises the needs for developing the integrated methods and software tools for facilitating structural health monitoring and the data analysis. This paper presents our latest research in sensor placement optimization, the data analytics for extracting useful information from conventional sensors, e.g. accelerometers and vison sensors, e.g. video cameras, conducting efficient data-driven model by deep learning, undertaking model calibration via parallel optimization and eventually empowering engineers for model-based asset management. The research leads to the development of the integrated software tools for various infrastructure industry applications, including but not limited to urban water systems, buildings and bridges. The use cases illustrate the benefits and new challenges of the expedited analysis, robust data-driven model, and monitoring-based performance modeling for the informed asset management decisions

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