Application of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweight

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dc.authorid0000-0001-7196-9407en_US
dc.authorid0000-0001-5702-807Xen_US
dc.contributor.authorÇalış, Gökhan
dc.contributor.authorYıldızel, Sadık Alper
dc.contributor.authorKeskin, Sultan
dc.date.accessioned2021-09-14T07:08:30Z
dc.date.available2021-09-14T07:08:30Z
dc.date.issued2021en_US
dc.departmentKMÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractConcrete is known as one of the fundamental materials in construction with its high amount of use. Lightweight concrete (LWC) can be a good alternative in reducing the environmental effect of concrete by decreasing the self-weight and dimensions of the structure. In order to reduce self-weight of concrete artificial aggregates, some of which are produced from waste materials, are utilized, and it also contributes to develop a sustainable material Artificial neural networks have been the focus of many scholars for long time with the purpose of analyzing and predicting the lightweight concrete compressive and flexural strengths. The artificial neural network is more powerful method in terms of providing explanation and prediction in engineering studies. It is proved that the error rate of ANN is smaller than regression method. Furthermore, ANN has superior performance over nonlinear regression model. In this paper, an ANN based system is proposed in order to provide a better understanding of basalt fiber reinforced lightweight concrete. In the regression analysis predicted vs. experimental flexural strength, R-sqr is determined to be 86%. The most important strength contributing factors were analyzed within the scope of this study.en_US
dc.identifier.citationÇalış, G., Yıldızel, S. A., Keskin, U. S. (2021). Application of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweight. Challenge Journal of Concrete Research Letters, 12(1), 12 - 19.en_US
dc.identifier.doi10.20528/cjcrl.2021.01.002
dc.identifier.endpage19en_US
dc.identifier.issn2548-0928
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85194848000
dc.identifier.scopusqualityN/A
dc.identifier.startpage12en_US
dc.identifier.trdizinid410918
dc.identifier.urihttps://hdl.handle.net/11492/5247
dc.identifier.urihttps://doi.org/10.20528/cjcrl.2021.01.002
dc.identifier.volume12en_US
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorÇalış, Gökhan
dc.institutionauthorYıldızel, Sadık Alper
dc.language.isoen
dc.relation.journalChallenge Journal of Concrete Research Lettersen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLightweight Concreteen_US
dc.subjectBasalt Fiberen_US
dc.subjectArtificial Neural Networken_US
dc.subjectCompressive Strengthen_US
dc.subjectStrength Predictionen_US
dc.titleApplication of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweighten_US
dc.typeArticle

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