Machine Learning Prediction Model for Shear Capacity of FRP-RC Slender and Deep Beams

Abstract

FPR reinforcing bars have emerged as a promising alternative to steel bars in construction, especially in corrosive environments. Literature includes several shear strength models proposed for FRP-RC members. This study presents a detailed evaluation of design shear models proposed by researchers and design codes. The evaluation was conducted through an extensive surveyed database of 388 FRP-RC beams without shear reinforcement tested in shear. Gene expression programming (GEP) has been utilized in this study to develop accurate design models for the shear capacity of slender and deep FRP-RC beams. Parameters used in the models are concrete compressive strength (f’c), section depth (d), section width (b), modular ratio (n), reinforcement ratio (ρf), shear span-to-depth ratio (a/d). The proposed model for slender beams resulted in an average tested-to-predicted ratio of 0.98 and a standard deviation of 0.21, while the deep beams model resulted in an average tested-to-predicted ratio of 1.03 and a standard deviation of 0.29. For deep beams, the model provided superior accuracy over all models. However, this can be attributed to the fact that the investigated models were not intended for deep beams. The deep beams model provides a simple method compared to the strut-and-tie method.