On an Optimization Framework for Damage Localization in Structures
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Efficient structural damage localization remains a challenge in structural health monitoring (SHM), particularly when the problem is coupled with uncertainty of conditions and complexity of structures. Traditional methods simply based on experimental data processing are often not enough reliable while complex models often struggle with computational inefficiency given the tremendous amount of model parameters. This talk focusses on closing the gap between data-driven probabilistic SHM and physics-based model updating, offering a solution for real-world infrastructure. We first concentrate on fusing multi-source damage-sensitive features (DSFs) based on experimental modal data into spatially mapped belief masses to pre-screen candidate damage locations. The resulting candidate dam- age locations are integrated into an inverse Finite Element Method (iFEM) model calibration process. We propose a limited memory BFGS optimization framework to identify the most probable damage scenario and validate it on an experimental beam model with single and multi- damage cases. We present the corresponding numerical results in this talk, which open the door to extend the application of the framework to a complex real bridge structure.
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