A multidimensional data-driven Bayesian network approach for predicting tunnel rock mass strength
WU Chen1, 2, HUANG Hongwei1*, NI Yiqing2, ZHOU Mingliang1
(1. Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; 2. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)
Abstract： Accurate prediction of tunnel rock mass strength is essential for ensuring construction safety. This study introduces a Bayesian network (BN)-based method that leverages multidimensional data. First, an enhanced Swin-Transformer, integrated with UNets capability for small-scale feature extraction, achieves high-precision segmentation of apparent rock mass features (e.g., leakage and fractures), attaining an average accuracy of 89.3%, which surpasses conventional models. Subsequently, a multidimensional dataset is constructed by integrating apparent features, internal parameters, physical-mechanical properties, and design parameters. Test results indicate that the constructed Bayesian network achieves an accuracy of 89.6%, outperforming traditional machine learning, deep learning, and experience-driven Bayesian network methods. It maintains an accuracy of 82.3% on untrained samples, demonstrating exceptional generalization capabilities. Sensitivity analysis reveals that fracture and weathering parameters are the dominant factors influencing rock strength. The proposed framework facilitates interactive operation and allows for partial parameter input, thereby providing a practical tool for intelligent tunnel engineering.
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2026, Vol. 45 
