Development of predictive models for shear strength of HSC slender beams without web reinforcement using machine-learning based techniques

Shear failure of slender beams made of High Strength Concrete (HSC) is one of the most crucial failures in designing reinforced concrete members. The accuracy of the existing design codes for HSC, unlike the Normal Strength Concrete (NSC) beams, seems to be limited in predicting shear capacity. This paper proposes a new set of shear strength models for HSC slender beams without web reinforcement using conventional multiple linear regression, advanced machine learning methods of Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) network. In order to achieve high-delity and robust regression models, this study employs a comprehensive database including 250 experimental tests. Various in uencing parameters, including the longitudinal steel ratio, shear span-to-depth ratio, compressive strength of concrete, size of the beam specimens, and size of coarse aggregate, are considered. The results indicate that the MARS approach has the best estimation in terms of both accuracy and safety aspects in comparison with regression methods and GMDH approach. Moreover, the accuracy and safety of predictions of MARS model is also remarkably more than the most common design equations. Furthermore, the robustness of the proposed models is conrmed through sensitivity and parametric analyses.