Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/314
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dc.contributor.authorÇalış, Gökhan-
dc.contributor.authorYıldızel, Sadık Alper-
dc.contributor.authorKeskin, Ülkü Sultan-
dc.date.accessioned2021-12-13T10:24:00Z-
dc.date.available2021-12-13T10:24:00Z-
dc.date.issued2021-
dc.identifier.issn2548-0928-
dc.identifier.issn2548-0928-
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TkRFd09URTRPQT09-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/314-
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 de?velop 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 understand?ing of basalt fiber reinforced lightweight concrete. In the regression analysis pre?dicted 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.language.isoenen_US
dc.relation.ispartofChallenge Journal of Concrete Research Lettersen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleApplication of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweighten_US
dc.typeArticleen_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.identifier.volume12en_US
dc.identifier.issue1en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.trdizinid410918en_US
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.02. Department of Civil Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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