Prediction of Chloride Resistance Level in Concrete Using Optimized Tree-Based Machine Learning Models

Abstract

The durability of reinforced concrete structures in chloride-rich environments remains a major concern in infrastructure design, particularly in coastal regions. While standardized laboratory procedures provide reliable quantification of chloride ingress resistance, they are often time-consuming, costly, and unsuitable for early-stage mix design. This study proposes a data-driven framework for predicting the chloride resistance level of concrete using tree-based machine learning (ML) classifiers. A comprehensive experimental dataset was utilized to train and validate three ML models: Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and CatBoost Classifier (CatBC). Extensive hyperparameter tuning was performed using the Optuna framework with 2000 trials per model to enhance predictive performance. Among the tested models, CatBC outperformed its counterparts with a test accuracy of 0.95 and weighted F1-score of 0.85. Feature importance analyses using SHAP values, Prediction Values Change, and other CatBoost interpretability tools consistently identified the water-to-binder ratio, superplasticizer content, test age, and aggregate proportions as key predictors of chloride resistance. The findings demonstrate that machine learning offers a fast, cost-effective, and accurate alternative for classifying concrete’s chloride resistance, supporting informed decision-making in mix design and service-life assessment.