Intelligent Tunnel Collapse Prediction Using Multi‐Modal Gaussian Cross‐Attention Fusion (MGCAF): Integration of TBM Parameters and Geological Radar Data

Concurrency and Computation Practice and Experience, vol. 38

Abstract

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.

Authors 6

1. Youliang Chen

   University of Shanghai for Science and Technology · UNSW Sydney

   Affiliation as printed

   Department of Civil Engineering University of Shanghai for Science and Technology Shanghai China

   Department of Engineering Geology, School of Civil and Environmental Engineering University of New South Wales Kensington New South Wales Australia

2. Wencan Guan corresponding Aachen Department of Engineering Geology and Hydrogeology

   University of Shanghai for Science and Technology · Bauhaus-Universität Weimar · RWTH Aachen University

   Affiliation as printed

   Department of Civil Engineering University of Shanghai for Science and Technology Shanghai China

   Department of Engineering Geology and Hydrogeology RWTH Aachen University Aachen Germany

   Institute of Structural Mechanics (ISM), Bauhaus University Weimar Weimar Germany

3. Rafig Azzam Aachen Department of Engineering Geology and Hydrogeology

   RWTH Aachen University

   Affiliation as printed

   Department of Engineering Geology and Hydrogeology RWTH Aachen University Aachen Germany

4. Suran Wang

   Tongji University · University of Shanghai for Science and Technology

   Affiliation as printed

   Department of Civil Engineering University of Shanghai for Science and Technology Shanghai China

   Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education Tongji University Shanghai China

5. Yungui Pan

   University of Shanghai for Science and Technology · Technische Universität Berlin

   Affiliation as printed

   Department of Civil Engineering University of Shanghai for Science and Technology Shanghai China

   Department of Engineering Geology, Institute of Applied Geosciences Technical University of Berlin Berlin Germany

6. Chao Yan

   University of Shanghai for Science and Technology

   Affiliation as printed

   Department of Civil Engineering University of Shanghai for Science and Technology Shanghai China