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A Study on the Estimation of Prefabricated Glass Fiber Reinforced Concrete Panel Strength Values with an Artificial Neural Network Model

S.A. Yıldızel1,2, A.U. Öztürk1
Manisa Celal Bayar University, Engineering Faculty, Turkey
Corresponding Author
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Computers, Materials & Continua 2016, 52(1), 41-52. https://doi.org/10.3970/cmc.2016.052.041

Abstract

In this study, artificial neural networks trained with swarm based artificial bee colony optimization algorithm was implemented for prediction of the modulus of rapture values of the fabricated glass fiber reinforced concrete panels. For the application of the ANN models, 143 different four-point bending test results of glass fiber reinforced concrete mixes with the varied parameters of temperature, fiber content and slump values were introduced the artificial bee colony optimization and conventional back propagation algorithms. Training and the testing results of the corresponding models showed that artificial neural networks trained with the artificial bee colony optimization algorithm have remarkable potential for the prediction of modulus of rupture values and this method can be used as a preliminary decision criterion for quality check of the fabricated products.

Keywords

Neural network, glass fiber reinforced concrete, glass fiber.

Cite This Article

S. . Yıldızel and A. . Öztürk, "A study on the estimation of prefabricated glass fiber reinforced concrete panel strength values with an artificial neural network model," Computers, Materials & Continua, vol. 52, no.1, pp. 41–52, 2016.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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