Abstract
This research employs conventional and optimized extreme gradient boosting (XGBoost) models to predict the end-bearing capacity ([Formula: see text]) of rock-socketed shafts. The arithmetic optimization (AOA), brainstorm optimization (BOA), and whale optimization (WOA) algorithms were used to optimize the XGBoost model. To conduct this research, a database of the [Formula: see text] of 151 rock-socketed shafts was compiled from the literature. The database (mentioned by O_Data) was preprocessed, and the [Formula: see text] of the 136 rock-socketed shafts was obtained. The Gaussian-noise technique was employed to create a synthetic database based on the [Formula: see text] of 136 rock-socketed shafts. A database of the [Formula: see text] of 500 rock-socketed shafts was generated and preprocessed. The [Formula: see text] of 460 rock-socketed shafts (136 original + 324 synthetic after preprocessing datasets) developed a second database (mentioned by OS_Data). The XGBoost, XGBoost_AOA, XGBoost_BOA, and XGBoost_WOA models were trained and tested using both databases. The performance analysis revealed that the XGBoost model estimated the [Formula: see text] with a root mean square error (RMSE) of 0.9205, mean absolute error of (MAE) of 0.7024, and a performance (R) of 0.9295 using the O_Data. Later, the performance of the XGBoost_AOA model was enhanced to 0.9894 using the OS_Data. It was also observed that OS_Data improved generalizability and reduced overfitting in the XGBoost_AOA model. Moreover, the multicollinearity analysis revealed that the rock mass rating (RMR) and geological strength index (GSI) exhibit problematic multicollinearity. In addition, the sensitivity analysis demonstrated that the RMR and GSI features have contributions of 20.301% and 20.369%, respectively, in estimating [Formula: see text]. For the first time, this research mapped a relationship between feature multicollinearity and sensitivity to analyze the overfitting of the soft computing models. Moreover, SHapley Additive exPlanations (SHAP) analysis identified compressive strength and rock mass rating as dominant predictors (0.65–1.36), while the geological strength index showed minimal influence (< 0.10). Finally, this research provides a Graphical User Interface application to help the geotechnical engineers and designers estimate the [Formula: see text].