Sayhood, E.K.; Mohammed, N.S.; Hilo, S.J.; Salih, S.S. Comprehensive Empirical Modeling of Shear Strength Prediction in Reinforced Concrete Deep Beams. Infrastructures2024, 9, 67.
Sayhood, E.K.; Mohammed, N.S.; Hilo, S.J.; Salih, S.S. Comprehensive Empirical Modeling of Shear Strength Prediction in Reinforced Concrete Deep Beams. Infrastructures 2024, 9, 67.
Sayhood, E.K.; Mohammed, N.S.; Hilo, S.J.; Salih, S.S. Comprehensive Empirical Modeling of Shear Strength Prediction in Reinforced Concrete Deep Beams. Infrastructures2024, 9, 67.
Sayhood, E.K.; Mohammed, N.S.; Hilo, S.J.; Salih, S.S. Comprehensive Empirical Modeling of Shear Strength Prediction in Reinforced Concrete Deep Beams. Infrastructures 2024, 9, 67.
Abstract
This paper presents a thorough investigation into the shear strength capacity of reinforced concrete deep beams, with a focus on improving predictive accuracy beyond existing code provisions. Analyzing 198 deep beams from 15 investigations, the study considers parameters such as concrete compressive strength (fc’), shear span to effective depth ratio (av/d), and reinforcement ratios (ρs, ρv, ρh ). Introducing a novel predictive model (Equation 7), the study rigorously evaluates it using nonlinear regression analysis and statistical metrics (MAE, RMSE, R2). The proposed model demonstrates a significant reduction in the coefficient of variation (COV) to 27.08%, surpassing existing codes' limitations. Comparative analyses highlight the model's robustness, revealing improved convergence of data points and minimal sensitivity to variations in key parameters. The findings suggest that the proposed model offers enhanced predictive accuracy across diverse scenarios, making it a valuable tool for structural engineers. This research contributes to advancing the understanding of shear strength in reinforced concrete deep beams, offering a reliable and versatile predictive model with implications for refining design methodologies and enhancing the safety and efficiency of structural systems.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
