A metaheuristic-driven categorical boosting framework with interpretability for high-precision prediction of mechanical properties in corroded reinforced concrete beams

Abstract

The degradation of mechanical properties in corroded reinforced concrete (RC) beams presents a major challenge for assessing structural durability. To address this issue, this study proposes an integrated machine learning (ML) framework to predict the mechanical properties of such beams. First, a database of 464 samples was established, including 12 input parameters and 2 output parameters, followed by correlation analysis of the inputs. On this basis, the applicability of existing design codes and empirical models was evaluated. Subsequently, eight ML models were trained, with their hyperparameters optimized via Bayesian optimization (BO) to enhance prediction accuracy. The Categorical Boosting (CatBoost) model was identified as the most accurate, and its hyperparameters were further optimized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for improved performance. Results show the PSO-optimized CatBoost model achieves the highest prediction accuracy to date: for the flexural strength test set, the coefficient of determination (R²) is 0.984 and root mean square error (RMSE) is 3.1602; for the deflection test set, R² is 0.975 and RMSE is 0.6259. Compared with design codes, flexural strength test set R² increases by 27.3 % and RMSE decreases by 72.8 %; versus traditional models like Support Vector Regression (SVR), R² rises by 5.4 % and RMSE drops by 43.5 %. Additionally, SHapley Additive exPlanations (SHAP) analysis reveals geometric parameters (beam height, beam width) dominate flexural strength, while elastic stiffness and beam length drive deflection. Finally, a user-friendly graphical user interface (GUI) was developed for rapid mechanical property assessment of corroded RC beams.