Application of XGBoost Model Optimized by Multi-Algorithm Ensemble in Predicting FRP-Concrete Interfacial Bond Strength

Accurate prediction of fiber-reinforced polymer (FRP)-concrete interfacial bond strength is critical for ensuring the safety of FRP-strengthened structures. This study proposes a predictive model based on extreme gradient boosting (XGBoost), which is enhanced via the Nevergrad optimization framework, to address the limited accuracy of traditional empirical approaches. By integrating seven optimizers from the Nevergrad platform, the model achieves global hyperparameter optimization, and a five-fold cross-validation strategy is employed to improve generalization. The prediction results based on 855 sets of single-lap shear test data demonstrate that the optimized model exhibits significantly superior performance on the test set (R² = 0.9726, RMSE = 1.8745, MAE = 1.3857). Compared to the existing best-performing empirical model, the R² is improved by 22.3%, while the RMSE and MAE are reduced by 63.4% and 61.8%, respectively. SHAP interpretability analysis indicates that the width, thickness, elastic modulus, and bond length of the FRP sheets are the main factors influencing the bond strength prediction. The predictive model developed in this study combines high accuracy with strong interpretability, providing a reliable, intelligent tool for designing FRP-strengthened structures.