Metaheuristic-optimized CatBoost models for flexural capacity prediction of reinforced UHPC beams: robustness assessment and SHAP interpretation
Predicting the flexural capacity of reinforced ultra-high-performance concrete (UHPC) beams remains challenging due to the nonlinear interactions among longitudinal reinforcement, sectional geometry, material strength, and fiber-related parameters. This study proposes an explainable metaheuristic-enhanced CatBoost framework in which hyperparameter optimization is systematically compared across four strategies: grid search, particle swarm optimization, grey wolf optimization (GWO), and whale optimization algorithm. A curated experimental database comprising 232 beam tests with 15 input variables was assembled from multiple independent studies. Among the four developed models, GWO-CatBoost achieved the best generalization performance in the single-run evaluation, with test R2 = 0.975 and MAE = 6.072 kN·m, MAPE = 9.876%, and RMSE = 8.462 kN·m, substantially outperforming simplified mechanics-based benchmark formulations derived from NF P18-710 and ACI 318–19. Repeated-seed analysis across 50 random seeds further confirmed GWO-CatBoost as the most robust model, yielding the highest mean test R2 (0.9564 ± 0.0179) and the lowest RMSE (12.81 ± 3.08 kN·m), with the smallest variability among all candidates. SHAP-based interpretation revealed that longitudinal reinforcement area is the dominant predictor (52.0%), followed by beam height (13.2%), steel yield strength (7.8%), compressive strength (6.8%), and beam width (6.4%). At the same time, fiber-related parameters exhibit comparatively minor contributions. Partial dependence analysis further confirmed the physical consistency of the learned relationships. The proposed GWO-CatBoost framework provides an accurate, robust, and interpretable data-driven tool for predicting the flexural capacity of reinforced UHPC beams, offering practical guidance for metaheuristic-based optimization in structural engineering applications.