TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion

Aiming to mitigate engineering risks such as tunnel face collapse and equipment jamming caused by poor geological conditions during the construction of tunnel boring machines (TBMs), this study proposes a TBM surrounding rock grade prediction method based on multi-source feature fusion. Firstly, a multi-source dataset is established by systematically integrating TBM tunnelling parameters, horizontal acoustic profile (HSP) detection data and three-dimensional geological spatial information. In the data preprocessing stage, the TBM data is cleaned and divided according to the mileage section, the statistical characteristics of key tunnelling parameters (thrust, torque, penetration, etc.) are extracted, and the rock fragmentation index (TPI, FPI, WR) is fused to construct a composite feature vector. The Direct-LiNGAM causal discovery algorithm is innovatively introduced to analyse the nonlinear correlation mechanism between multi-source features, and then a hybrid model, TRNet, which combines the local feature extraction ability of convolutional neural networks and the nonlinear approximation advantages of Kolmogorov–Arnold networks, is constructed. Verified by a real tunnel project in western Sichuan, China, the prediction accuracy of TRNet for surrounding rock grade on the test set reaches an average of 92.15%, which is higher than other data-driven methods. The results show that the prediction method proposed in this paper can effectively predict the surrounding rock grade of the tunnel face during TBM tunnelling, and provide decision support for the dynamic regulation of tunnelling parameters.