Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs

Abstract

With the emergence of fiber-reinforced polymer (FRP) reinforcement as a substitute for conventional steel reinforcement, different design codes have been developed to account for the different mechanical properties of the FRP, specifically the elastic modulus. The ACI 440.11-22, CSA/S806-12, and JSCE-97 are well-known standards for FRP-reinforced concrete structures. In particular, these design standards show significant variations in estimating the punching shear resistance and accounting for the elastic modulus. This study provides a statistical and machine learning-based evaluation of the punching shear models in design standards. An Artificial neural network (ANN) framework is used to develop a generalized punching resistance model of flat slabs reinforced with steel and FRP bars utilizing a large experimental dataset with 539 tests. The study presents a parametric study to examine the effect of different factors on the punching shear strength. The parametric study provided a graphical presentation and comparison between the design models and the developed ANN model.