Abstract:Damage identification has always been one of the core functions of bridge structural health monitoring (SHM) systems. Damage identification techniques based on deep learning (DL) approaches have shown great promise recently. However, DL methods still need to be improved owing to their poor interpretability and generalization performance. The fundamental reason lies in the separation between physics-based mechanical principles and data-driven DL methods. To address this issue, this paper proposes a physics-insp… Show more
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