Enhanced Prediction of Bond Strength in Corroded RC Structures Using Advanced Feature Selection and Ensemble Learning Framework

Bond behavior between steel bars and concrete is fundamental to the structural integrity and durability of reinforced concrete. However, corrosion-induced deterioration severely impairs bond performance, highlighting the need for advanced and reliable assessment methods. This paper pioneers an algorithm for an advanced ensemble learning framework to predict bond strength between corroded steel bars and concrete. In this framework, a novel Stacked Boosted Bond Model (SBBM) is developed, in which a Fusion-Based Feature Selection (FBFS) strategy is integrated to optimize input variables, and SHapley Additive exPlanations (SHAP) are employed to enhance interpretability. A merit of the framework is that it can effectively identify critical factors such as crack width, transverse confinement, and corrosion level, which have often been neglected by traditional models. The proposed SBBM achieves superior predictive performance, with a coefficient of determination (R2) of 0.94 and a mean absolute error (MAE) of 1.33 MPa. Compared to traditional machine learning and analytical models, it demonstrates enhanced accuracy, generalization, and interpretability. This paper provides a reliable and transparent tool for structural performance evaluation, service life prediction, and the design of strengthening measures for corroded reinforced concrete structures, contributing to safer and more durable concrete structures.