ABSTRACT Tunnel face instability prediction represents a critical technical challenge in underground engineering, particularly during tunnel boring machine (TBM) excavation under complex geological conditions. This study proposes the Multi‐modal Gaussian Cross‐Attention Fusion (MGCAF) algorithm, which integrates physics‐constrained Gaussian processes with cross‐attention mechanisms to achieve intelligent tunnel collapse prediction. The MGCAF framework reconstructs the traditional prediction paradigm by treating earth pressure balance chamber pressure as the primary prediction target rather than an input parameter, while incorporating first‐principles constraints of TBM cutting mechanisms into kernel function design. The algorithm employs a dual‐pathway architecture that fuses TBM operational parameters through temporal modeling, processes geological radar images via deep feature extraction, and achieves cross‐modal information fusion through physics‐constrained cross‐attention mechanisms. Dynamic kernel optimization enables real‐time adaptive parameter adjustment through multi‐source gradient feedback. Validation results based on the Yinsong Water Diversion Tunnel project (20 km length, 9 collapse events) demonstrate that the algorithm achieves high‐precision prediction with R 2 = 0.8330, successfully predicting major collapse locations with approximately 20‐m accuracy. Comparative analysis against baseline methods (Transformer, Gaussian Process, Random Forest, XGBoost) indicates that MGCAF exhibits superior performance in engineering reliability (0.95) and ROC‐AUC (0.765) metrics. Generalization testing on the 2025 Los Angeles Wilmington Sewage Outfall Tunnel confirms the algorithm's cross‐domain applicability. Ablation experiments reveal that the cross‐attention mechanism serves as the primary performance driver, while uncertainty quantification provides interpretable risk assessment for TBM operations in heterogeneous geological environments.
Liu et al. (Thu,) studied this question.