CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments.

Accurate prediction of the carbonation depth of concrete is critical to avoid structural damage and ensure durability. However, predicting carbonation depth remains challenging due to the complexity of the process, interdependencies among material parameters, and varying environmental conditions. In this study, we propose a hybrid Correlation-Driven Feature Generation (CFG) framework enhanced by a novel Dynamic Weighted Correlation (DWC) method to optimize feature selection and improve machine learning (ML) predictions. Using our new dataset of concrete samples exposed to natural conditions, we evaluated traditional correlation methods (Spearman, Pearson, Kendall's tau) alongside Dynamic Weighted Correlation (DWC), combined with ML algorithms (Linear Regression, Random Forest, and XGBoost). The DWC method dynamically weights feature segments to capture both linear and non-linear relationships, generating new correlated features that significantly enhance model performance. Statistical metrics (R², MSE, RMSE, MAE) confirmed the superiority of DWC, with XGBoost achieving the highest prediction accuracy (R² = 0.86, 20.5% MAE reduction). Concrete age emerged as the most influential parameter across all methods. Our results demonstrate that the CFG-DWC framework not only outperforms conventional correlation techniques but also provides a robust, interpretable tool for carbonation depth prediction, enabling more durable design and maintenance of reinforced concrete structures.