Bridge Deflection Prediction Based on Cascaded Residual Smoothing and Multiscale Spatiotemporal Attention Network

Bridge deflection values are significant for their health and safety, but current methods for predicting bridge deflection suffer from problems such as anomalous data and low prediction accuracy. To solve the problems of anomalous bias and loss of short-term trend in traditional smoothing methods, this paper proposes a preprocessing method for cascade residual smoothing. The method firstly uses Gaussian filtering to initially remove the high-frequency noise in the signal and retain the overall trend. Then, the residuals of the initial filtering and the original data are smoothed by quadratic exponential smoothing to extract the short-term trend in the deflection data, which is favorable for the data to have the advantages of both stabilization and retention of small fluctuations. In addition, to simultaneously acquire the temporal dependence and spatial features between long- and short-term temporal signals, this paper proposes a multiscale spatial attention network based on Multiscale Convolutional Neural Networks (MSCNNs), Gated Recurrent Units (GRUs), and self-attention (SA). The method obtains multi-level sensory field spatial information within each period through the MSCNN, focuses on the connection between different time steps using a GRU, and employs SA to automatically focus on the deflection features that have a significant impact and ignore unimportant perturbation variations, thus improving the prediction ability of the model. In this paper, compared with CNN-Attention-LSTM, the MAE is reduced by 25.79%, the RMSE is reduced by 24.69%, and the R2 is increased by 2.36%, which proves the superiority and advancement of the method.