Damage‐Detection Approach for Bridges with Multi‐Vehicle Loads Using Convolutional Autoencoder

Deep learning has been widely employed in recent studies on bridge‐damage detection to improve the performance of damage‐detection methods. Unsupervised deep learning can be effec-tively utilized to increase the applicability of damage‐detection approaches. Hence, the authors pro-pose a convolutional‐autoencoder (CAE)‐based damage‐detection approach, which is an unsupervised deep‐learning network. However, the CAE‐based damage‐detection approach demonstrates only satisfactory accuracy for prestressed concrete bridges with a single‐vehicle load. Therefore, this study was performed to verify whether the CAE‐based damage‐detection approach can be applied to bridges with multi‐vehicle loads, which is a typical scenario. In this study, rigid‐frame and rein-forced‐concrete‐slab bridges were modeled and simulated to obtain the behavior data of bridges. A CAE‐based damage‐detection approach was tested on both bridges. For both bridges, the results demonstrated satisfactory damage‐detection accuracy of over 90% and a false‐negative rate of less than 1%. These results prove that the CAE‐based approach can be successfully applied to various types of bridges with multi‐vehicle loads.