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

  • Bahman O. Taha Erbil Technical Engineering College Erbil, Kurdistan Region.
  • Peshawa J. Muhammad Ali Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
  • Haval A. Ahmed Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
DOI: https://doi.org/10.14500/aro.10066
Keywords: Concrete, fiber reinforced bars, fiber reinforced polymer (FRP), 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.

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Author Biographies

Bahman O. Taha, Erbil Technical Engineering College Erbil, Kurdistan Region.
Department of Civil Engineering
Peshawa J. Muhammad Ali, Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
Peshawa J. Muhammad Ali is a lecturer and researcher at the Department of Software Engineering, Koya University since 2006. He has B.Sc. in Civil Engineering and M.Sc. in Computer Science. His main research area is data mining and machine learning with several published articles in the field of neural networks. Mr. Peshawa is a Consultant Civil Engineer at the Kurdistan Engineering Union and he has an experience in this field.
Haval A. Ahmed, Department of Software Engineering, Faculty of Engineering, Koya University, University Park, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region.
Haval A. Ahmed received B.Sc. and M.Sc. degrees in Software Engineering from Salahaddin University-Erbil, in 2007 and 2014, respectively. From 2009 to 2011, he was a teaching assistant in the Software Engineering Department at Salahaddin University-Erbil. Currently, he is an assistant lecturer in the Department of Software Engineering at Koya University. His research interests include neural networks, fuzzy systems, computer vision, face detection and recognition, and open source technology. Mr. Haval is a practitioner Software Engineer at the Kurdistan Engineers Union, Iraq

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ARO Journal: Volume 3, No. 2 (2015)
Published
2015-06-25
How to Cite
Taha, B. O., Muhammad Ali, P. J. and Ahmed, H. A. (2015) “Optimizing the Flexural Strength of Beams Reinforced with Fiber Reinforced Polymer Bars Using Back-Propagation Neural Networks”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 3(2), pp. 1-10. doi: 10.14500/aro.10066.
Issue
Vol. 3 No. 2 (2015): Issue Five
Section
Articles

Copyright (c) 2015 Bahman O. Taha, Peshawa J. Muhammad Ali, Haval A. Ahmed

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