Seismic damage states prediction of in-service bridges using feature-enhanced swin transformer without reliance on damage indicators

Abstract

To achieve real-time and efficient evaluation of seismic damage to structures, this study proposes an improved deep learning-based model, the deep feature-enhanced Swin Transformer (CC-SwinT). This model overcomes the influence of service life on the time-varying damage indicators of bridges. By eliminating the reliance on seismic performance and damage indicators, it predicts the seismic damage state of in-service bridges based solely on the response of structure. The CC-SwinT model integrates continuous wavelet transform (CWT) technology and the context anchored attention (CAA) mechanism to enhance the extraction of structure response features of bridge piers. This integration enables the model to effectively mine time-frequency characteristics and capture non-local long-term dependencies in structure responses. To comprehensively train the CC-SwinT model, a structure response database for in-service bridges was constructed based on a data-driven objectives, analyzing the impacts of service conditions on the seismic performance of bridges. Subsequently, transfer learning methods were applied, and the performance of the CC-SwinT framework was evaluated using various metrics to highlight its exceptional feature extraction and prediction capabilities. Furthermore, the Gradient-weighted Class Activation Mapping (Grad-CAM) interpretability technique was used to explore the decision-making process and feature focus of CC-SwinT. The findings of this study provide a valuable reference for seismic damage prediction of in-service structures and rapid post-earthquake rescue response.