Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models

Accurate prediction of tunnel squeezing, one of the common geological hazards during tunnel construction, is of great significance for ensuring construction safety and reducing economic losses. To achieve precise prediction of tunnel squeezing, this study constructed six reliable machine learning (ML) classification models for this purpose, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbors (KNN). The parameters of these 6 ML models were optimized using the Whale Optimization Algorithm (WOA) in conjunction with five-fold cross-validation. A total of 305 tunnel squeezing sample data were collected to train and test the models. KNN and Synthetic Minority Over-sampling Technique (SMOTE) methods were employed to handle the missing and imbalanced data sets. An input feature system for tunnel squeezing prediction was established, comprising tunnel burial depth ( H ), tunnel diameter ( D ), strength-to-stress ratio ( SSR ), and support stiffness ( K ). The XGBoost model optimized with WOA demonstrated the highest prediction accuracy of 0.9681. The SHAP method was utilized to interpret the XGBoost model, indicating that the contribution rank of the input features to tunnel squeezing prediction was SSR > K > D > H , with average SHAP values of 2.93, 1.49, 0.82, and 0.69, respectively. The XGBoost model was applied to predict tunnel squeezing in 10 sections of the Qinghai Huzhu Beishan Tunnel. The prediction results were highly consistent with the actual outcomes.