A TCN–BiLSTM–Logarithmic Attention Hybrid Model for Predicting TBM Cutterhead Torque in Excavation

To enhance intelligent decision-making for tunneling operations in complex geological conditions, this study proposes a high-precision prediction method for TBM cutterhead torque using engineering data from the west return-air roadway of the Shoushan No. 1 Mine in Pingdingshan, Henan (China). A multisource dataset integrating geological exploration data, TBM electro-hydraulic parameters, and surrounding rock–TBM interaction indicators was constructed and preprocessed through outlier removal, interpolation restoration, and Savitzky–Golay filtering to extract high-quality steady-state features. To capture the mechanical properties of composite strata, the equivalent strength parameter of composite strata and an integrity-classification index were introduced as key predictors. Based on these inputs, a hybrid TCN–BiLSTM–Logarithmic Attention model was developed to jointly extract local temporal patterns, model global dependencies, and emphasize critical operating responses. Testing results show that the proposed model consistently outperforms TCN, BiLSTM, and TCN-BiLSTM baselines under intact, transitional, and fractured rock conditions. It achieves an RMSE (19.85) and MAPE (3.72%) in intact strata, while in fractured strata RMSE (29.55) and MAPE (10.82%) are reduced by 23.5% and 22.7% relative to TCN. Performance in transitional strata is likewise superior. Overall, the TCN–BiLSTM–Logarithmic Attention model demonstrates the highest prediction accuracy across intact, transitional, and fractured strata; effectively captures the mechanical characteristics of composite formations; and achieves robust and high-precision prediction of TBM cutterhead torque in complex geological environments.