Artificial intelligence-based prediction of chloride migration coefficient in concrete with enhanced data imputation, optimization, and explainability

Accurate quantification of the non-steady-state chloride migration coefficient (D_nssm) is vital for evaluating the durability and service life of concrete structures exposed to chloride environments. This study introduces an explainable machine learning (ML) framework for predicting D_nssm, integrating robust data imputation, systematic hyperparameter optimization, and model explainability. Seven ML algorithms were evaluated, with a Gradient Boosting model optimized via grid search achieving the highest predictive performance (MAE = 3.302 × 10⁻¹² m²/s, RMSE = 8.519 × 10⁻¹² m²/s, R² = 0.896). Bayesian optimization delivered comparable performance with a 65–95% reduction in computation time, demonstrating its efficiency for scalable model calibration. Advanced imputation techniques preserved incomplete yet informative observations, enhancing data completeness and model generalizability without compromising predictive performance. Model explainability analysis using SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) revealed water content, concrete age, superplasticizer dosage, cement content, and cement type as the most influential features, collectively explaining ∼65% of the model's variance. In addition, a user-friendly graphical interface was developed to support both single and batch predictions, enhancing accessibility for both research and engineering use. The proposed framework provides reasonably accurate and explainable data-informed tool for supporting chloride transport assessment and durability-oriented decision-making in concrete engineering.