Effect of Freeze–Thaw Cycles on Bond Properties at the FRP-Concrete Interface: Experimental Evaluation and Machine Learning Prediction

Fiber-reinforced polymer (FRP)–concrete bonding is widely adopted for structural strengthening, yet its durability is highly vulnerable to freeze–thaw (FT) degradation. This study combines experimental testing with interpretable machine learning (ML) to reveal the degradation mechanism and predict the interfacial behavior of FRP–concrete systems under FT exposure. Single-lap shear tests showed that all specimens failed through interfacial debonding accompanied by partial concrete peeling. The ultimate bond strength decreased by 6.0–18.5%, and the peak shear stress dropped by 53–80%, indicating a pronounced loss of ductility and adhesion. To extend the analysis, experimental data were integrated with literature datasets, and three ensemble ML algorithms—AdaBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to predict key bond–slip parameters including ultimate bond strength, local maximum bond stress, slip values, and interfacial fracture energy. Among them, XGBoost achieved the highest predictive accuracy, with R2 values exceeding 0.94 for most output parameters and consistently low RMSE values. Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) further identified adhesive tensile strength, fiber modulus, FRP thickness, and concrete strength as dominant factors and defined their optimal ranges. The findings offer a scientific foundation for evaluating and predicting the long-term bond durability of FRP–concrete systems and support the development of reliable reinforcement strategies for infrastructure in cold and severe environments.