A study on the estimation of prefabricated glass fiber reinforced concrete panel strength values with an artificial neural network model

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dc.contributor.authorYildizel S.A.
dc.contributor.authorÖztürk A.U.
dc.date.accessioned2024-07-22T08:12:03Z
dc.date.available2024-07-22T08:12:03Z
dc.date.issued2016
dc.description.abstractIn 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. © 2016 Tech Science Press.
dc.identifier.DOI-ID10.3970/cmc.2016.052.041.pdf
dc.identifier.issn15462218
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/15874
dc.language.isoEnglish
dc.publisherTech Science Press
dc.subjectBackpropagation
dc.subjectBackpropagation algorithms
dc.subjectBending (forming)
dc.subjectConcrete buildings
dc.subjectConcrete slabs
dc.subjectConcretes
dc.subjectFiber reinforced materials
dc.subjectFibers
dc.subjectGlass
dc.subjectGlass fibers
dc.subjectNeural networks
dc.subjectOptimization
dc.subjectPrecast concrete
dc.subjectArtificial bee colony optimization algorithms
dc.subjectArtificial bee colony optimizations
dc.subjectArtificial neural network modeling
dc.subjectDecision criterions
dc.subjectFour-point bending test
dc.subjectGlass fiber reinforced concrete
dc.subjectModulus of raptures
dc.subjectModulus of rupture
dc.subjectReinforced concrete
dc.titleA study on the estimation of prefabricated glass fiber reinforced concrete panel strength values with an artificial neural network model
dc.typeArticle

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