Leveraging Interpretable Ensemble Machine Learning for Predicting Interfacial Bond Strength Between Normal-Strength Concrete Substrate and UHPC Overlays

The interfacial bond strength between the normal strength concrete (NSC) substrate and ultra-high-performance concrete (UHPC) overlays exhibits crucial significance for the longevity and structural integrity of existing or damaged structures. The effectiveness of better repair and retrofitting of NSC is contingent upon the ability of the UHPC-NSC interface to establish a resilient bond with each other under diverse conditions. So, continuous monitoring and assessing the interfacial bond strength with higher prediction accuracy becomes essential for preserving the integrity of NSC structures. Despite numerous empirical formulations, accurately capturing the factors affecting bond strength has remained elusive. In this study, split tensile and slant shear strength of the UHPC-NSC interface are predicted using five data-driven ensemble machine learning algorithms: gradient boosting regressor, adaboost, LightGBM, Xgboost, and Catboost. Notably, gradient boosting regression and Catboost consistently outperformed other models, demonstrating high R² as 0.963 and 0.827 and low RMSE as 0.351 and 3.137 in testing sets. Nested cross-validation and Bayesian optimization techniques are incorporated for hyperparameter tuning to enhance model robustness. Additionally, the study incorporated Shapley additive explanations plots to reveal the complex relationships between the variables across both local and global scopes, consequently enhancing their viability and interpretability. The results obtained from the SHAP plots unveiled the substantial influence of surface treatment and other factors on bond strength. Further, the study introduced a reverse design approach to elucidate the factors influencing bond strength, guiding future concrete rehabilitation design schemes with a comprehensive understanding of the relationship between input and target features.