Three neural networks for prestressed fiber-reinforced polymer/plastics sheet-reinforced concrete beams

Abstract

This paper introduces a method for predicting the flexural ultimate load of prestressed fiber-reinforced polymer/plastics sheet-reinforced concrete beams. The main work of this paper is as follows: three artificial neural network models of back propagation, Levenberg-Marquardt, and Bayesian Regularization predict the flexural ultimate loads of prestressed FRP-RC and were constructed using a database of 243 concrete beams from various previous studies. The optimal hidden layer node of the neural network is determined as 17 using MSE value, R-value and average error as evaluation metrics. The optimal value of each parameter in the neural network is determined using MSE value as metrics. A series of simulations showed that the Bayesian Regularization model with 17 nodes in the hidden layer best matches the experimental results with root mean square error and correlation coefficient (R) values of 0.002256 and 0.97972, respectively, and an average error of 8.79%. The contribution analysis of the input variables indicates that the flexural capacity of the RC beam was most significantly influenced by its width, elastic modulus, and lower reinforcement, with relative importance of 10.38%, 9.88%, and 9.56%, respectively. Noisy data in the input layer is eliminated using Singular Value Decomposition, optimizing the performance of neural networks.

Keywords

• prestressed frp sheet
• reinforced concrete
• flexural load capacity
• neural network
• contribution analysis