Capacity Prediction of RC Beams Strengthened with FRP by Artificial Neural Networks Based on Genetic Algorithm

Document Type : Regular Article

Author

Department of Civil Engineering, University of New South Wales, Sydney, Australia

Abstract

In this paper, the ability of the artificial neural network which was trained based on a Genetic algorithm used to predict the shear capacity of the reinforced concrete beams strengthened with the side-bonded fiber reinforced polymer (FRP). A database of experimental data including 95 data which were published in literature was collected and used to the network. Seven inputs including the width of the beam, effective depth, FRP thickness, Young modulus, the tensile strength of FRP and also FRP ratio were used to predict the shear capacity of the reinforced concrete beams strengthened with the side-bonded fiber reinforced polymer. The best values of the weights and the biases were obtained by the Genetic algorithm. For increasing the ability of the model to predict the considered target, it was suggested that the predicted values considered smaller. The results indicated that the proposed neural network based on genetic algorithm was able to predict the shear capacity of the considered elements.

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  • Receive Date: 28 June 2017
  • Revise Date: 15 July 2017
  • Accept Date: 15 July 2017

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