|Source||CMC: Computers, Materials & Continua, Vol. 52, No. 1, pp. 41-52, 2016|
|Download||Full length paper in PDF format. Size = 1,211,883 bytes|
|Keywords||Neural network; glass fiber reinforced concrete; glass fiber.|
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.