Bridge damage detection framework based on multi-scale feature fusion and multi-attention enhanced residual network

Advanced structural health monitoring (SHM) systems have emerged as a critical requirement for ensuring structural integrity and operational safety. However, only limited SHM data are utilized in traditional machine learning approaches for structural damage detection tasks, while complementary multisource data might be ignored. This study presents a bridge damage detection framework that integrates multi-attention mechanisms and cross-modal feature learning. The proposed method can address the shortcomings of traditional approaches in multisource heterogeneous data fusion and feature extraction. The framework enhanced feature extraction from multisource vibration signals and suppression of redundant information, which benefits from the parallel channel-spatial attention and residual compression excitation mechanisms. Furthermore, both global and local features were simultaneously captured from the fused multichannel time–frequency representations based on a multi-scale feature fusion strategy. The effectiveness of the proposed method was validated through the datasets from a laboratory-scale cable-stayed bridge under moving vehicle loading. The numerical results demonstrate the superior performance and robustness of the proposed method compared to traditional methods in identifying various damage patterns under noisy conditions.