Machine learning models for predicting carbonation depth in fly ash concrete: performance and interpretability insights

This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete, overcoming the limitations of traditional predictive methods. Five ensemble-based models, such as adaptive boosting (AdaBoost), categorical boosting (CatBoost), gradient boosting regressor (GBR), hist gradient boosting regressor (HistGBR), and extreme gradient boosting (XGBoost), were developed and optimized using 729 high-quality dataset points incorporating seven input parameters, including cement, CO 2 , exposure time, water-binder ratio, fly ash, curing time, and compressive strength. Several performance evaluation metrics were used to compare the models. The GBR model emerged as the best-performing model, based on high coefficient of determination ( R 2 ) values and balanced error metrics across both validation and testing datasets. While all models performed exceptionally well on the training data, GBR demonstrated superior generalization capability, with R 2 values of 0.9438 on the validation set and 0.931 on the testing set. Furthermore, its low mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MdAE) confirmed its robustness and accuracy. Moreover, SHapley Additive exPlanations (SHAP) analysis enhanced the interpretability of predictions, highlighting the curing time and exposure time as the most critical drivers of carbonation depth.