Modeling the concrete carbonation depth using a physics-guided CatBoost algorithm

Purpose This study aims to develop a physics-informed machine learning framework to improve carbonation depth prediction in reinforced concrete by integrating mechanistic knowledge with data-driven learning. Design/methodology/approach A CatBoost model incorporating ten physics-guided features was trained on a data set expanded to twice the original training set via physics-constrained augmentation and validated using multiple train-test splits. Findings The proposed model achieved R2 = 0.996 and RMSE = 3.59 mm. The physics-based carbonation coefficient kestimate was the dominant predictor, contributing 55.3% of the normalized SHapley Additive exPlanations importance, while engineered physics features collectively accounted for 77% of the predictive contribution. Research limitations/implications The model requires further validation against independent natural-exposure data sets to ensure long-term reliability beyond accelerated testing conditions. Practical implications This framework shows potential for supporting carbonation-depth assessment within the experimental domain represented by the available data. Further validation using independent natural-exposure data sets is required before its application to long-term service-life assessment. Social implications Improved durability predictions contribute to public safety and environmental sustainability by extending the lifespan of infrastructure and reducing the carbon footprint associated with premature reconstruction. Originality/value The proposed framework bridges deterministic carbonation theory with explainable machine learning, providing a physically interpretable approach for carbonation-depth assessment within the investigated experimental domain.