Opening-Closing Prediction of Immersed Tube Tunnel Joints Based on SSA-LSTM-WNN

The prediction of joint opening-closing deformations helps improve the operational safety of immersed tunnel systems. Addressing issues such as the poor accuracy and limited applicability of existing models, a novel method for predicting the joint opening-closing deformations of immersed tunnels is proposed. Firstly, the Sparrow Search Algorithm (SSA) is used to adaptively optimize the hyperparameters of the Long Short-Term Memory (LSTM) network, constructing an SSA-LSTM model to achieve feature learning and preliminary prediction of the deformation information. Based on this, the Ljung-Box (LB) test is employed to analyze the residual sequences, and a Wavelet Neural Network (WNN) is introduced to further extract effective information from the residual sequences, obtaining corrected values for the residual sequences. Finally, the prediction results from the SSA-LSTM model and the corrected residual sequence results are superimposed and reconstructed to obtain the predicted values of the joint opening-closing deformations. The proposed method is validated based on the immersed tube tunnel project of the Hong Kong-Zhuhai-Macao Bridge. The results show that: The proposed model exhibits excellent overall prediction performance, effectively mining useful information from the deformation sequences and having an advantage in considering the local features of the deformation sequences. The final prediction achieve an R~2 of 0.9994, RMSE of 0.007 2 mm, and MAE of 0.006 6 mm, which are higher in accuracy and stability compared to LSTM, SSA-LSTM, and traditional SVR, BP, and XGBoost models. This method can serve as a means to deeply explore the development and variation patterns of joint opening-closing deformations.