📝 SummarXiv

Abstract

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 sufficiently reliable, while complex models often struggle with computational inefficiency given the tremendous amount of model parameters. This paper focuses on closing the gap between data-driven SHM and physics-based model updating by offering a solution for real-world infrastructure. We first concentrate on fusing multi-source damage-sensitive features (DSF) based on experimental modal data into spatially mapped belief masses to pre-screen candidate damage locations. The resulting candidate damage locations are integrated into an inverse Finite Element method (FEM) model calibration process. We propose an optimization framework to identify the most probable damage scenario with single and multi-damage cases. We present the corresponding numerical results in this paper, which open the door to extend the application of the framework to a complex real bridge structure.