Optimizing the Flexural Strength of Beams Reinforced with Fiber Reinforced Polymer Bars Using Back-Propagation Neural Networks

Abstract

The reinforced concrete with fiber reinforced polymer (FRP) bars (carbon, aramid, basalt and glass) is used in places where a high ratio of strength to weight is required and corrosion is not acceptable. Behavior of structural members using (FRP) bars is hard to be modeled using traditional methods because of the high non-linearity relationship among factors influencing the strength of structural members. Back-propagation neural network is a very effective method for modeling such complicated relationships. In this paper, back-propagation neural network is used for modeling the flexural behavior of beams reinforced with (FRP) bars. 101 samples of beams reinforced with fiber bars were collected from literatures. Five important factors are taken in consideration for predicting the strength of beams. Two models of Multilayer Perceptron (MLP) are created, first with single-hidden layer and the second with two-hidden layers. The two-hidden layer model showed better accuracy ratio than the single-hidden layer model. Parametric study has been done for two-hidden layer model only. Equations are derived to be used instead of the model and the importance of input factors is determined. Results showed that the neural network is successful in modeling the behavior of concrete beams reinforced with different types of (FRP) bars.