Modeling the bond–slip behavior of the interface between a stiffened core and cemented soil based on machine learning approaches

The bond–slip behavior of stiffened deep cement mixing (SDCM) piles—which is crucial for their bearing capacity—evolves continuously with curing age. In the study reported here, 20 element tests were conducted on the interface between cemented soil and a stiffened core, analyzing the bond–slip behavior affected by curing temperature and age, and then ensemble learning methods (XGBoost, random forest) were used to establish models for the evolution of the bond–slip behavior considering thermal effects. The constructed models can predict the peak shear strength (τ_max), the residual shear strength (τ_res), and the interfacial shear modulus (G). The test results show that the shear strength of the stiffened-core–cemented-soil interface grows with the increasing curing temperature and age, with faster growth at 0–14 days compared to 60–90 days. To lessen the reliance on ineffective brute-force searching, Bayesian optimization with a tree-structured Parzen estimator is used to select the hyperparameters of the established models. The results demonstrate the superior performance of the chosen approach, with R² > 0.93 for the training set and R² > 0.81 for the test set. The results of the XGBoost model are best for τ_max, with a mean absolute percentage error of less than 5 %, thereby enabling accurate predictions of the mechanical parameters of the stiffened-core–cemented-soil. This research enhances the understanding of the mechanical properties of SDCM piles and provides valuable guidance for projects involving such piles.