Research on the Identification of Bridge Structural Damage Using Variational Mode Decomposition and Convolutional Self-Attention Neural Networks

Convolutional neural networks (CNN) are widely used for structural damage identification. However, the presence of environmental disturbances introduces noise into the acquired acceleration response data, impairing the performance of CNN models. In this study, we apply empirical mode decomposition (EMD) and variational mode decomposition (VMD) to denoise the data from a steel truss bridge. By comparing the smoothness and convergence of the obtained modal functions (IMFs) using EMD and VMD, we confirm the effectiveness of VMD in smoothing and denoising the bridge structure signals. Additionally, we propose a convolutional self-attention neural network (CSANN) model to extract features and identify damage in the denoised data using VMD. Comparative analysis of the CNN, LSTM, and GRU models reveals that the VMD-CSANN model outperforms the others in terms of damage localization and identification accuracy. It also exhibits excellent performance when handling noise-contaminated data with a noise level of 10%. These findings demonstrate the efficacy of the proposed method for identifying internal damage in steel truss structures, while maintaining smoothness and robustness during processing.

Automated summary

In this study, empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to denoise the data from a steel truss bridge. The effectiveness of VMD in smoothing and denoising the bridge structure signals is confirmed by comparing the obtained modal functions using EMD and VMD. Additionally, a convolutional self-attention neural network (CSANN) model is proposed for feature extraction and damage identification using VMD. The VMD-CSANN model outperforms other models in terms of damage localization and identification accuracy, and exhibits excellent performance when handling noise-contaminated data with a noise level of 10%.