Concrete Carbonization Prediction Method Based on Bagging and Boosting Fusion Framework

Concrete carbonation is an important factor causing corrosion of steel reinforcement, which leads to damage to reinforced concrete structures. To address the problem of concrete carbonation depth prediction, this paper proposes a prediction model. The framework synergistically integrates Bagging and Boosting algorithms, specifically replacing the original Random Forest base learner with gradient Boosting variants (LightGBM (version 4.1.0), XGBoost (version 2.1.1), and CatBoost (version 1.2.5)). This hybrid approach exploits the strengths of all three algorithms to reduce variance and bias, and to further improve prediction accuracy, Bayesian optimization algorithms were used to fine-tune the hyperparameters, resulting in three hybrid-integrated models: Random Forest–LightGBM Fusion Framework, Random Forest–XGBoost Fusion Framework, and Random Forest–CatBoost Fusion Framework. These models were trained on a dataset containing 943 case sets and six input variables (FA, t, w/b, B, RH, and CO2). The models were comprehensively evaluated using the comprehensive scoring formula and Taylor diagrams. The results showed that the hybrid-integrated model outperformed the single model, with the RF–CatBoost fusion framework having the highest test set performance (R2 = 0.9674, MAE = 1.4199, RMSE = 2.0648, VAF = 96.78%). In addition, the Random Forest–CatBoost Fusion Framework identified exposure t and CO2 concentration as the most important features. This paper demonstrates the applicability of a predictive model based on the Random Forest–CatBoost Fusion Framework in predicting the depth of concrete carbonation, providing valuable insights into the durability design of concrete.