Hybrid machine learning models and simplified design formulations for predicting punching shear strength in internal SFRC slab-column connections

This study aims to develop and validate advanced machine learning models for predicting the punching shear strength of steel fiber-reinforced concrete (SFRC) slab-column connections. Using a curated dataset of 377 experimental results, 36 outliers were identified and removed using Cook's distance approach, which resulted in a refined dataset of 341 samples. Predictive frameworks were constructed employing artificial neural networks (ANN), Categorical Boosting (CatBoost), and a hybrid Auto search optimization (ATOM)-ANN approach. The performance of these models was rigorously evaluated against established design formulas to assess their predictive accuracy and robustness. The CatBoost model outperformed its counterparts, achieving a mean absolute error (MAE) of 0.007 and a coefficient of determination of 0.921 on the validation set, demonstrating its superior ability to handle highly nonlinear relationships. SHAP analysis identified critical factors influencing punching shear strength , including concrete strength, fiber volume content, reinforcement ratio, slab thickness , and column dimensions. Results highlighted optimal reinforcement ratios between 1.5 % and 1.7 %, beyond which fiber congestion compromises strength. Additionally, four nonlinear design models were introduced, with Model-2 providing the best performance among the proposed formulations. Benchmarking against existing design methods revealed moderate predictive capabilities , but their limitations in addressing nonlinear behaviors underscore the need for advanced approaches. • Novel ML-driven models predict punching shear strength in SFRC slab-column connections. • CatBoost algorithm shows exceptional proficiency, handling categorical features robustly. • SHAP analysis identifies key factors influencing punching shear strength predictions. • Developed nonlinear design models outperform existing empirical formulations. • Taylor diagram visually illustrates superior predictive accuracy of ML models.