A Machine Learning-Enhanced Hybrid Framework to Quantify Rebar Effects on Chloride Ingress

Accurate quantification of the apparent chloride diffusion coefficient is pivotal for predicting the service life and assessing the durability of coastal infrastructure. While existing empirical models are informative, they fundamentally overlook the influence of reinforcement. This study establishes an integrated computational framework combining XGBoost machine learning with finite element (FE) analysis to elucidate chloride transport mechanisms in reinforced concrete (RC), explicitly accounting for the presence of reinforcement. Based on 171 experimental datasets, this study developed a prediction model to estimate the apparent chloride diffusion coefficient in reinforced concrete subjected to dry–wet cycles. The diameter of rebar was innovatively incorporated as a parameter, systematically integrating seven other key influencing factors into the model. Shapley Additive Explanations (SHAP) analysis reveals that exposure duration, sampling depth, coarse-to-total aggregate ratio, and rebar diameter constitute the dominant influencing parameters. Furthermore, FE analysis reveals that the presence of rebar redistributes aggregates, forming preferential pathways that increase chloride concentration at the steel-concrete interface. This study shows that the influence of reinforcement on chloride diffusion cannot be ignored. The proposed methodology advances durability science by data-driven modeling with physics-based modeling, providing actionable strategies for marine infrastructure optimization.