The physical and mechanical properties of the soil-structure interface under the freeze-thaw condition are complex, making empirical shear strength models poorly applicable. This study employs integrated machine learning algorithms to model the shear behavior of frozen-thawed silty clay and the structure interface. A series of direct shear tests have been conducted under high normal stress and freeze-thaw conditions using an improved direct shear test system (DRS-1). The test data obtained were used to train and validate a classification and regression tree (CART)-based integrated model. Through cross-validation, the model's optimal hyperparameters were determined on the training set, and its performance was then verified on the test set. The results indicated that the proposed integrated learning models closely match the experimental data. The accuracy of the CART-based model on the training set is R2 = 0.994, while the accuracy on the test set is R2 = 0.763. High pressure and freeze-thaw temperature were identified as key factors influencing the trend of shear stress-strain curves. The CART-based model offers a scientific basis for predicting the shear behavior of the frozen-thawed soil-structure interface.
