Prediction of Freeze–Thaw Damage in Shallow-Buried Bias Tunnels Using Thermal–Mechanical Modeling and Machine Learning

To evaluate freeze–thaw damage and asymmetric deterioration in shallow-buried bias tunnel linings in cold regions, an explainable data-driven predictive framework was developed. Guided by the underlying frost heave mechanisms, an optimal support vector machine (SVM) surrogate model was trained using 200 thermal–mechanical coupled samples generated via Latin hypercube sampling (LHS). The model achieved high predictive accuracy, yielding an R2 exceeding 0.98 and a root-mean-square error (RMSE) of only 0.036. Using the Shapley additive explanations (SHAP) algorithm to deconstruct damage evolution mechanisms, results revealed that ambient temperature exerts a stable quasi-linear driving effect on localized tensile stress concentrations in the lining, with an equivalent deterioration gradient of approximately 0.0731 MPa/°C. Meanwhile, cumulative freeze–thaw cycles trigger a steady quantitative accumulation of lining structural deformation. Results indicate that prior to material yield, structural deterioration of tunnel linings under severe cold conditions does not manifest as an abrupt nonlinear transition, but rather as a highly stable quasi-linear accumulation. This finding clarifies the safety evolution trajectory during the early- to mid-term service stages of extreme cold tunnel linings, providing a robust quantitative basis for proactive tunnel operational early warning systems.