Abstract

Chloride-induced corrosion is one of the principal causes of deterioration in reinforced concrete infrastructure, making accurate prediction of chloride diffusion coefficients essential for durability assessment and service-life design. Existing machine learning models often suffer from limited experimental datasets and insufficient incorporation of engineering knowledge, restricting their predictive capability and generalization. This study presents a physics-guided machine learning framework that integrates domain-informed feature engineering, conditional synthetic data augmentation, and stacking ensemble learning to predict the chloride diffusion coefficient of concrete from Rapid Chloride Migration (RCM) test data. Physics-guided features were developed to represent fundamental transport mechanisms and binder characteristics, while synthetic data augmentation was employed to improve data coverage and enhance model robustness. The final stacking ensemble combined CatBoost, XGBoost, Random Forest, and Linear Regression through a Ridge Regression meta-learner. The proposed framework achieved a coefficient of determination (R2) of 0.903, with an RMSE of 1.321 and an MAE of 0.920 on an independent holdout dataset, outperforming all individual machine learning models. Ablation analysis demonstrated that synthetic data augmentation was the primary contributor to performance improvement, while ensemble learning provided additional gains in predictive accuracy and robustness. Model interpretability using SHapley Additive exPlanations (SHAP) identified slag content, water-to-binder ratio, and porosity-related variables as the dominant factors governing chloride diffusion predictions, consistent with established durability mechanisms. The proposed framework provides an accurate and interpretable tool for chloride diffusion prediction that supports durability assessment, service-life estimation, and the design of sustainable concrete mixtures.

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