Assessment of shear strength of steel fiber reinforced concrete (SFRC) beams using gene expression programming

Abstract

This study focuses on to predict the ultimate shear strength of steel fiber reinforced concrete (SFRC) slender and deep beams without stirrups using the gene expression programming (GEP). For this purpose, a large database containing 437 shear strength tests of SFRC beams that failed in shear mode were divided into two different groups according to their span-depth ratios and two different models were developed for slender and deep beams. The effective depth of beam (d), longitudinal reinforcement ratio (rho), shear span to effective depth ratio (a/d), compressive strength of concrete (f(c)'), fiber volume percentage V-f)and fiber factor (F) were considered as input parameters in GEP models. The results of the proposed models were compared to those of the existing models in the literature. It was concluded that the proposed models provide the best performance and accuracy for the shear strength of both slender and deep beams. Furthermore, sensitivity and parametric analysis were performed separately to evaluate the influence of input parameters on the shear strengths of both slender and deep SFRC beams.