Abstract: To address the challenges of low support efficiency and high surrounding rock deformation risk during full-face tunnel boring machine (TBM) tunneling in soft rock deformation zones, a risk identification method based on an integrated attention convolutional neural network-long short-term memory (ACNN-LSTM) model is developed. The proposed approach aims to achieve accurate hierarchical early warning of soft rock deformation risks. A case study is conducted on a plateau railway tunnel, and a closed-loop control system of “perception-prediction-decision” is constructed. Core learning data are selected from TBM tunneling parameters and geological parameters through Grey correlation analysis; the risk of soft rock deformation is classified into five levels based on the convergence monitoring values of surrounding rock deformation and support structures; data features are extracted via CNN neural networks, and the attention mechanism is employed to enhance LSTM’s ability to capture temporal dynamics, achieving risk identification for TBMs traversing soft rock deformation sections. The research results show that the overall accuracy of the ACNN-LSTM model reaches 90.91%, with a macro accuracy of 90.77% and a recall rate of 88.66%. Its comprehensive prediction performance is significantly better than that of the CNN-LSTM and LSTM models. The attention mechanism effectively enhances the capture of key features and reduces information decay in long sequences; the model exhibits different overall accuracy under different risk classification methods, indicating that label classification approaches affect model performance and generalization ability. The research results indicate that the ACNN-LSTM model can provide a data-driven basis for support decision-making during the construction of soft rock deformation sections in deep-buried TBM tunnels.

Key words: full-face tunnel boring machine, soft rock deformation, Grey correlation analysis, attention convolutional neural network-long short-term memory (ACNN-LSTM) model, CNN-LSTM model, LSTM model