Residual correction-based dam deformation monitoring method via data dimension reduction and optimized Bi-LSTM algorithm

As the most direct and effective evaluation indicator, dam displacements are often utilized to reflect the behavior of dam structures under external environments and loads. The statistical regression dam monitoring model has the disadvantages of limited generalization ability and weak robustness in dealing with complex dam deformation behavior prediction problems. To solve these problems, this study combines artificial intelligence and deep learning (DL) methods to propose a high-precision hybrid forecasting model considering residual correction. Specifically, kernel principal component analysis is utilized to reduce the data dimension of high-dimensional prototypical monitoring data. The statistical regression algorithm and improved hydraulic-thermal-time are combined to develop the overall trend of dam deformation sequences. Then, the residual components that cannot be effectively explained by traditional statistical models are fed into DL-based algorithms to learn the underlying relationship. Specifically, a bidirectional long short-term memory neural with the self-attention mechanism network is used to learn the residual distribution law, and the random search optimization algorithm is used for determining the optimal parameters. A high arch in long-term service with massive prototypical monitoring data is introduced as the case study, and the three typical dam displacement monitoring points are utilized as research items. The experimental results show that the method can fully combine the interpretability of the traditional statistical regression method and the nonlinear modeling ability of the DL-based method, and has achieved good performance in the deformation prediction of high arch dams.