Predicting self-healing efficiency in recycled aggregate concrete using optimized machine learning models.

ABSTRACT: Self-healing concrete can repair microcracks and enhance durability, but traditional methods are costly and complex. Using recycled coarse aggregate (RCA) as a substitute for natural aggregates can reduce costs, improve self-healing properties, and support sustainable construction. This study applied machine learning (ML) to predict the impact of RCA on concrete's self-healing performance. A database of 173 datasets was established, with eight key indicators as input variables and the self-healing rate as the output. An optimized NRBO-XGBoost model was developed and compared with four ML models and two optimization methods. The NRBO-XGBoost model outperformed all others, achieving the highest accuracy (R² = 0.9569, RMSE = 7.1800, MAE = 4.9575). Sensitivity analysis using the Shapley method

SUBMITTER: Cao K 

PROVIDER: S-EPMC12546734 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature