Prediction of the Shear Strength of Concrete Beams Reinforced with Fiber Reinforced Polymer Bars Using Artificial Neural Networks Model

Abstract

In this paper an Artificial Neural Networks (ANNs) model is developed to predict the shear strength of concrete beams reinforced with fiber reinforced polymer (FRP) bars. An experimented data set collected from the experimental studies on  concrete beams reinforce with FRP bars are used in the artificial neural network. They are arranged in a format of six input parameters including the width and depth of beams, compressive strength of concrete, modulus of elasticity, reinforcement ratio of FRP and the shear span to depth ratio and one output parameter which is shear strength. A parametric study is carried out using ANN to study the influence of each parameter on the shear strength of concrete beams reinforced with fiber reinforced polymers; the results showed that the shear strength increases with increasing all parameters used in ANN model except the shear span to depth ratio. In this case, as the shear span to depth ratio decreases, the shear strength increase. The results of this study indicate that the ANN provides good prediction as compared to the experimental data and the empirical equations.