Machine learning-based prediction of shear and flexural capacity of reinforced concrete beams: a parallel modeling framework with SHAP interpretability and design code benchmarking

Introduction Design-code equations for the shear and flexural capacity of reinforced concrete (RC) beams are conservative and variable across structural conditions. This study develops a parallel modeling framework — two structurally independent machine-learning pipelines, one trained only on shear-failure specimens and one only on flexure-failure specimens, each using an identical set of four algorithms (gradient boosting [GB], random forest, support vector regression, and artificial neural network). Methods The pipelines were trained on 487 shear and 412 flexural beam specimens obtained from physical laboratory tests reported in the peer-reviewed literature. Model predictions were benchmarked against the unfactored characteristic equations of Eurocode 2 (EC2), ACI 318-19, and fib Model Code 2010, and were interpreted with SHAP. Results The top-performing GB model achieved R² = 0.963 (shear) and R² = 0.971 (flexure), with more than 36% lower RMSE than the best-performing code. All three codes were systematically conservative, especially at low reinforcement ratios and high shear-span-to-depth ratios. SHAP identified mode-specific feature rankings: the shear-span-to-depth ratio dominated shear (Kani-valley behaviour), and the longitudinal reinforcement ratio dominated flexure (saturation near the balanced ratio). Discussion The results support mode-specific parallel pipelines for physically interpretable capacity prediction and offer guidance for future code calibration and practical assessment/retrofit applications.