Shield Thrust Time-Series Prediction Based on BiLSTM with Intelligent Hyperparameter Optimization

Shield thrust is a key control parameter for ensuring the safety and efficiency of tunnel construction. Under complex geological conditions and strong data nonlinearity, conventional prediction methods often fail to achieve sufficient accuracy. This study proposes a hybrid prediction model in which a bidirectional long short-term memory (BiLSTM) network is optimized by intelligent algorithms. A multidimensional input dataset comprising tunnel geometry, geomechanical parameters and tunnelling parameters is constructed, and BiLSTM is used to capture bidirectional temporal dependencies in the tunnelling data. To adaptively determine key BiLSTM hyperparameters (number of neurons, dropout rate and learning rate), four intelligent optimization algorithms—genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA) and Hunger Games Search (HGS)—are employed for hyperparameter tuning, using the root-mean-square error (RMSE) between predicted and measured values as the fitness function. Four hybrid models, GA–BiLSTM, PSO–BiLSTM, SSA–BiLSTM and HGS–BiLSTM, are validated using real engineering data from Beijing Metro Line 22, Guanzhuang–Yongshun shield-driven section. The results show that HGS–BiLSTM outperforms the other models in terms of RMSE, mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R²) and exhibits faster convergence, supporting real-time prediction and decision-making in shield thrust control.