Designing a robust extreme gradient boosting model with SHAP-based interpretation for predicting carbonation depth in recycled aggregate concrete

The degradation of concrete structures is significantly influenced by carbonation, where atmospheric carbon dioxide (CO2) penetrates the concrete matrix. Measuring how far carbonation penetrates into concrete plays a vital role in maintaining structural integrity and construction safety standards. Precisely forecasting the extent of carbonation penetration in recycled aggregate concrete (RAC) remains fundamental for understanding long-term performance and durability. This research is the first to introduce an innovative approach that leverages eight machine learning algorithms to estimate carbonation penetration depth. The selected techniques include NGBoost, GBRT, AdaBoost, CatBoost, XGBoost, LightGBM, HistGBRT, and MLR. Moreover, to evaluate model accuracy, four key performance indicators were employed. Additionally, SHapley Additive exPlanations (SHAP) was incorporated for enhanced model interpretability. Furthermore, the investigation examined six distinct input parameter configurations during training and testing to thoroughly assess model performance. Among the evaluated algorithms, XGBoost delivered the highest accuracy, with an RMSE of 1.389 mm, MAE of 1.005 mm, and R of 0.984. CatBoost followed closely, with RMSE of 1.772 mm, MAE of 1.344 mm, and R of 0.976. Then, the LightGBM ranked third in effectiveness, exhibiting an RMSE of 1.797 mm, MAE of 1.296 mm, and R of 0.975. These results demonstrate the reliability and interpretability of advanced machine learning models for carbonation depth estimation in RAC. The developed models offer practical tools for engineers seeking to evaluate how carbonation penetration affects structural integrity. These findings establish a strong foundation for understanding and predicting carbonation-related deterioration in concrete infrastructure.