A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

Based on data from the Jilin Water Diversion Tunnels from the Songhua River (China), an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine (TBM) cutter-head torque is presented. Firstly, a function excluding invalid and abnormal data is established to distinguish TBM operating state, and a feature selection method based on the SelectKBest algorithm is proposed. Accordingly, ten features that are most closely related to the cutter-head torque are selected as input variables, which, in descending order of influence, include the sum of motor torque, cutter-head power, sum of motor power, sum of motor current, advance rate, cutter-head pressure, total thrust force, penetration rate, cutter-head rotational velocity, and field penetration index. Secondly, a real-time cutter-head torque prediction model's structure is developed, based on the bidirectional long short-term memory (BLSTM) network integrating the dropout algorithm to prevent overfitting. Then, an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed. Early stopping and checkpoint algorithms are integrated to optimize the training process. Finally, a BLSTM-based real-time cutter-head torque prediction model is developed, which fully utilizes the previous time-series tunneling information. The mean absolute percentage error (MAPE) of the model in the verification section is 7.3%, implying that the presented model is suitable for real-time cutter-head torque prediction. Furthermore, an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling. Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that: (1) the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%, and both the coefficient of determination (R²) and correlation coefficient (r) between measured and predicted values exceed 0.95; and (2) the incremental learning method is suitable for real-time cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.