Structural damage identification based on physics-guided deep learning: applications to large-scale structures

Deep learning (DL)-based structural damage identification recently attracts significant attention in the area of structural health monitoring (SHM). These methods are usually characterized as black-box models. The reliability of the model relies on the quantity and quality of data used to train it, which is often not available in practice. To assist the model training, development of physics-guided neural network (PGNN), that is, combining physics laws and data in training the model, is becoming a more and more popular researched topic and approach. However, the existing studies on structural damage identification using PGNN face the problems of poor generalization ability and lack of application to the large-scale structures. In order to overcome these challenges, based on modal sensitivity analysis of large-scale structures and model reduction, a new physics-based loss function is proposed and incorporated into the plain DL model to form a novel physics-guided DL (PGDL) framework. Two large-scale structures, including a numerical continuous rigid frame bridge and the tested I-40 steel-concrete composite bridge, are adopted to verify the feasibility and effectiveness of the proposed PGDL framework. The effect of common interferences, including random noise and sparse measurement, is also investigated to examine the noise-robustness of the proposed approach. The results demonstrate that the proposed approach has a superior damage localization and quantification performance than the plain DL model under the effect of multiple interferences. The proposed framework not only provides a better solution for damage identification of large-scale structures but also enriches the research scope of applying PGNN for SHM.