A novel hybrid model for predicting the end‑bearing capacity of rock‑socketed piles

It is of great significance to accurately evaluate the end-bearing capacity of rock-socketed piles based on multiple parameters. This study presents a hybrid model coupling extreme gradient boosting (XGBoost) with Bayesian optimization (BO) method for predicting the end-bearing capacity of rock-socketed piles. 138 data samples collected from the literature were used to construct the model. Five parameters, unconfined compressive strength of intact rock σ_c, geological strength index GSI, pile length within the soil layer H_s, pile length within the rock layer H_r and pile diameter B, are used as the input variables. The BO-XGBoost model was compared with two empirical formulas, as well as random forest (RF), gene expression programming (GEP), back propagation neural network (BPNN) and group method of data handling (GMDH). The results show that the coefficient of determination, root mean squared error and mean absolute error of the hybrid BO-XGBoost model for all datasets are 0.963, 0.634 and 0.240, respectively, and the accuracy order of these models is BO-XGBoost > RF > GEP > BPNN > GMDH > empirical formulas. In addition, the sensitivity analysis results show that σ_c is the most important parameter for predicting the end-bearing capacity of rock-socketed piles.