Freezing depth prediction of surrounding rock in seasonally frozen tunnels based on bayes-optimized XGBoost

During freeze-thaw cycles, frost heave forces induced by pore water phase transition in the surrounding rock of seasonally frozen tunnels act continuously on the lining structure, impairing its stability and service performance. The evolution of freezing depth is governed by the coupling effects of temperature, moisture and surrounding rock properties, and directly correlates with the distribution of frost heave forces. Thus, accurate prediction of freezing depth is critical for frost heave disaster control and structural design of cold-region tunnels. In this study, moisture migration and phase transition during surrounding rock freezing were considered to establish a hydrothermal coupling model and identify key influencing factors. On this basis, a dataset of tunnel freezing depth in seasonally frozen regions was constructed by combining Latin Hypercube Sampling (LHS) with numerical simulation methods. Subsequently, a Bayes-optimized XGBoost (BO-XGBoost) prediction model was constructed, where Bayesian optimization (BO) was employed for hyperparameter tuning. Distinguished from traditional manual or grid search methods, this algorithm realizes automatic and precise parameter optimization with higher efficiency, and effectively avoids the model from falling into local optimality during the tuning process. SHapley Additive exPlanations (SHAP) analysis was used to quantify feature contributions, and the Stefan solution verified prediction reliability. Results show that BO-XGBoost outperforms Random Forest (RF), Support Vector Regression (SVR) and Light Gradient Boosting Machine (LightGBM), with high sensitivity to temperature, moisture and thermal conductivity properties but low sensitivity to specific heat properties. Increased thermal conductivity of surrounding rock and lining deepens the predicted freezing depth, while higher specific heat capacity, initial moisture content, initial rock temperature and average temperature of the coldest month reduce it. The maximum relative error between model predictions and field monitoring data is only 7.9%, significantly lower than the 22.4% of the Stefan solution, providing a more accurate method for predicting the freezing depth of seasonally frozen tunnels.