Abstract: Interaction loads (shield thrust and cutterhead torque) between rock and shield are essential for equipment design and tunneling efficiency. Existing theoretical and statistical models involve numerous parameters and are computationally cumbersome. Further research is required to improve the efficiency and accuracy of load assignment in the digital design and digital twin of the shield. Although machine learning algorithms emphasize the proportion and mechanical parameters of geological formation, they often overlook the spatial relationships of strata. To address this, a data-driven load prediction method is proposed between rock and machine interaction based on a convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM), and an attention mechanism to characterize multifeature information from images and data sequences. First, the K-means algorithm is employed to extract and slice the geological longitudinal profile. Features are extracted from the sliced images using CNN, and a multifeature fusion dataset is formed by combining equipment operation data. Second, the BiLSTM network is employed to extract bidirectional time series information of the input features, and the attention mechanism is used to redistribute the weights of subsequent features. Finally, the grey wolf optimizer is used to optimize the hyper parameters of the deep learning network. The results indicate that the GWO-CNN-BiLSTM-Attention model exhibits better predictive performance for shield thrust and cutterhead torque—with R² values exceeding 0.9 and an average relative error below 1%—than the BP, PSO-BP, and LSTM models. By incorporating geological spatial information, the proposed model offers improved applicability for load prediction in rock-machine interactions.

Key words: super-large diameter slurry shield; interaction between rock and machine, load, data driven, multifeature, grey wolf optimizer