Abstract: The authors conduct a case analysis of a large-diameter slurry shield tunnel crossing the Yangtze river in Wuhan, China. The Bayesian optimization algorithm (BO) is employed to optimize key hyperparameters in the prediction model for tunnel face pressure, which is based on graph convolutional networks (GCN) and long shortterm memory (LSTM) neural networks. Furthermore, the Shapley additive explanations (SHAP) method is applied to globally interpret the prediction model and calculate the Shapley value of each input parameter for the prediction target, enhancing the model′s interpretability and transparency. The research findings are as follows: (1) The proposed BOGCN-LSTM method demonstrates high accuracy across all historical time steps, achieving an average goodness of fit (R²), root mean square error (E_RMSE), mean absolute error (E_MAE), and mean absolute percentage error (E_MAPE)of 0.943, 0.245, 0.173, and 1.183%, respectively. (2) Among historical time steps t-1 to t-10, the metrics at time step t-3—R² of 0.953, E_RMSE of 0.233, E_MAEof 0.159, and E_MAPE of 1.151%—show the best overall predictive performance, with a computational running rate of 1.7 times per second. (3) The global interpretation results using the SHAP method indicate that the air cushion chamber pressure, inlet and outlet slurry pressures, and cutterhead squeezing pressure difference significantly influence the research objectives, offering valuable decision-making insights for controlling tunnel face pressure in large-diameter slurry shield operations. The BO-GCN-LSTM deep learning model effectively predicts tunnel face pressure, assisting shield tunneling operators in making informed parameter adjustments.

Key words: large-diameter slurry shield, hybrid deep learning, tunnel face pressure, Bayesian optimization-graph convolutional networks-long short-term memory neural networks, Shapley additive explanations