Prediction of shear strength of FRP-reinforced concrete members using a rule-based method

Abstract

Due to sudden and brittle shear failure of concrete members reinforced with fibre-reinforced polymer (FRP), shear design of these members is necessary. Various design equations have been developed to determine the shear strength with and without stirrup members. However, there is still no clear expression to predict the shear strength of FRP-reinforced concrete and the available design formulas have limited accuracy. Recently, soft computing methods such as artificial neural networks have been used for predicting the shear strength of FRP-reinforced concrete elements. However, these methods do not give enough insight into the generated models and are not as easy to use as the empirical formulas. In this study, new formulas based on M5′ and multivariate adaptive regression splines (MARS) model tree approaches for the prediction of shear strength are presented. In order to develop new models, a comprehensive database containing 176 and 112 test data for members with and without stirrups, respectively, is used. It is shown that the proposed models are compact, simple and physically sound. The most important parameters are specified based on sensitivity analysis, which is calculated using the MARS algorithm. Comparison between the developed and shear design formulas showed that the developed models are more accurate than existing equations.