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

Bahman O. Taha, Peshawa J. Muhammad Ali, Haval A. Ahmed


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.


Concrete, fiber reinforced bars, fiber reinforced polymer (FRP), neural networks

Full Text:



Al-Shamaa, M.F.K., 2010. Behaviour of Lightweight Concrete Beams Reinforced with Fibre Reinforced Polymer Bars, PhD. University of Technology Baghdad, Iraq.

Al-Sunna, R.A.S., 2006. Deflection Behaviour of FRP Reinforced Concrete Flexural Members, PhD. University of Sheffield.

Barris, C. et al., 2009. An experimental study of the flexural behaviour of GFRP RC beams and comparison with prediction models. Composite Structures, 91, pp.286-95.

Benmoktane, B., Chaallalt, O. and Masmoudi, R., 1995. Flexure Response of Concrete Beams Reinforced with FRP Reinforcing Bars. ACI Structural Journal, 9(2), pp.46-55.

Chitsazan, I., Kobraei, M., Jumaat, M.Z. and Shafig, P., 2010. An experimental study on the flexural behavior of FRP RC beams and a comparison of the ultimate moment capacity with ACI. Civil Engineering and Construction Technology, 1(2), pp.27-42.

Duranovic, N., Pilakoutas, K. and Waldron, P., 1997. Tests on Concrete Beams Reinforced with Glass Fiber Reinforced Plastic Bars. In Third International Symposium on Non-metallic (FRP) Reinforcement for Concrete Structures. Sapporo, Japan, 1997. Japan Concrete Institute.

Hall, M. et al., 2009. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1), pp.10-18. Available at: http://www.cs.waikato.ac.nz/~ml/weka/index.html .

Lee, S. and Lee, C., 2014. Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. Engineering Structures, 61, pp.99-112.

Leung, C.K.Y., Ng, M.Y.M. and Luk, H.C.Y., 2006. Empirical Approach for Determining Ultimate FRP Strain in FRP-Strengthened Concrete Beams. Journal of Composites for Construction, 10(2), pp.125-38.

Mashrei, M.A., Seracino, R. and Rahman, M.S., 2013. Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints. Journal of Construction and Building Materials, 40, pp.812-21.

Masmoudi, R., Theriault, M. and Benmokrane, B., 1998. Flexural Behavior of Concrete Beams Reinforced with Deformed Fiber Reinforced Plastic Reinforcing Rods. ACI Structural Journal, 96(6), pp.665-76.

Metwally, I.M., 2013. Prediction of Punching Shear Capacities of Two-way Concrete Slabs Reinforced with FRP Bars. HBRC Journal, 9, pp.125-33.

Muhammad Ali, P.J., 2014. Predicting the Gender of the Kurdish Writers in Facebook. Sulaimani Journal for Engineering Sciences, 1(1), pp.18-28.

Muhammad Ali, P.J., Surameery, N.M.S., Yunis, A.M. and Abulrahman, L.S., 2013. Gender Prediction of Journalists from Writing Style Style. Aro, the Scientefic Journal of Koya University, 1(1), pp.22-28. Retrieved from http://dx.doi.org/10.14500/aro.10031.

Perera, R., Tarazona, D., Ruiz, A. and Martín, A., 2014. Application of artificial intelligence techniques to predict the performance of RC beams shear strengthened with NSM FRP rods. Formulation of design equations. Journal of Composites: Part B, 66, pp.162-73.

Taha, B.O., 2013. Flexural Response of High Strength Concrete Beams Reinforced with CFRP Rebars, PhD. University of Salahadeen, Erbil, Iraq.

Toutanji, H.A. and Saafi, M., 2000. Flexural Behavior of Concrete Beams Reinforced with Glass Fiber-Reinforced Polymer (GFRP) Bars. ACI Structural Journal, 97(5), pp.712-19.

Yousif, S.T. and Al-Jurmaa, M.A., 2010. Modeling of ultimate load for R.C. beams strengthened with Carbon FRP using artificial neural networks. Al-Rafidain Engineering Journal, 18(6), pp.28-41.

DOI: http://dx.doi.org/10.14500/aro.10066
View Counter: Abstract | 490 | and PDF | 299 |

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


  • There are currently no refbacks.

Copyright (c) 2016 Bahman O. Taha, Peshawa J. Muhammad Ali, Haval A. Ahmed

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


ARO Journal is an OAJ supported by Koya University, it has no article submission/processing charges (APCs).
© 2013-2019, Koya University is a public University accredited by the Ministry of Higher Education and Scientific Research, KRG - F.R. Iraq.