Interpretable prediction of dynamic compressive strength of red sandstone under wetting-drying cycles using ensemble learning.

With geotechnical engineering facing more complex environmental challenges, accurately predicting the mechanical behavior of rocks under dynamic loading becomes essential for maintaining structural safety. In this study, the dynamic compressive strength (DCS) of red sandstone samples under the coupled acidic wetting-drying cycles was investigated, and a dataset comprising 597 test results of experimental samples was obtained. Subsequently, five input variables (strain rate, number of wetting-drying cycles, pH values, uniaxial compressive strength, and P-wave velocity) were used to develop and validate five ensemble learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Gradient Boosting Decision Tree (GBDT). The performance of the models was evaluated using four metrics: R², RMSE, MAE, and MAPE, while SHapley Additive exPlanations (SHAP) were employed to interpret feature significance. Among the evaluated models, XGBoost and LightGBM showed similarly strong predictive performance, with XGBoost yielding slightly better overall accuracy under the present dataset conditions. SHAP analysis indicated that strain rate was the most influential factor in DCS prediction, whereas uniaxial compressive strength contributed relatively less. In addition, a graphical user interface (GUI) was developed based on the XGBoost model to facilitate intuitive prediction and visualization of DCS under acidic wetting-drying conditions. Overall, the proposed framework provides an effective and interpretable tool for predicting rock dynamic strength in chemically degraded environments and may support geotechnical stability evaluation and risk reduction.