Explainable Ensemble Machine Learning Models for Bond Strength Prediction at Ultra-High-Performance-to-Normal-Strength-Concrete Interfaces

Abstract

The slant shear bond strength of the UHPC-NSC interface is a key parameter governing load transfer and structural reliability in composite concrete members. However, accurate prediction remains challenging because of strong nonlinear relationships among influencing parameters and the limited availability of experimental data. This study presents an optimized machine learning framework for reliable bond strength prediction by integrating Extra Trees Regressor (ETR) and CatBoost (CATB) with Grasshopper Optimization (GO) and Northern Goshawk Optimization (NG) for hyperparameter optimization. Model performance was evaluated using cross-validation and independent testing to ensure reliable generalization. Among the developed models, the optimized CATB-NG achieved the highest predictive accuracy with an R2 of 0.934, RMSE of 3.081 MPa, and MAE of 2.186 MPa. SHapley Additive exPlanations (SHAP) identified NSC surface treatment and compressive strength as the dominant factors influencing bond strength, while Individual Conditional Expectation (ICE) analysis revealed nonlinear feature interactions and threshold behaviors. To facilitate practical engineering applications, the optimized model was implemented in a graphical user interface (GUI) for real-time prediction with standardized feature encoding. The proposed framework provides an accurate, interpretable, and user-friendly tool for predicting UHPC-NSC interfacial bond strength and supports engineering design and decision making.