Acceleration Response Reconstruction for Structural Health Monitoring Based on Fully Convolutional Networks

Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring (SHM). However, traditional methods struggle to address the reconstruction of acceleration responses with complex features, resulting in a lower reconstruction accuracy. This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks (FCN) to achieve precise reconstruction of acceleration responses. In the designed network architecture, the incorporation of skip connections preserves low-level details of the network, greatly facilitating the flow of information and improving training efficiency and accuracy. Dropout techniques are employed to reduce computational load and enhance feature extraction. The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinear mapping relationship between the input and output responses. Finally, the accuracy of the FCN for structural response reconstruction was evaluated using acceleration data from an experimental arch rib and compared with several traditional methods. Additionally, this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge. Through parameter analysis, the feasibility and accuracy of aspects such as available response positions, the number of available channels, and multi-channel response reconstruction were explored. The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains., with performance surpassing that of other networks, confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.