Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

Basit Öğe Kaydı
dc.authorid0000-0001-7320-8409
dc.contributor.authorSaridemir, Mustafa
dc.contributor.authorTopcu, Ilker Bekir
dc.contributor.authorOezcan, Fatih
dc.contributor.authorSevercan, Metin Hakan
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractIn this study, artificial neural networks and fuzzy logic models for prediction of long-term effects of ground granulated blast furnace slag on compressive strength of concrete under wet curing conditions have been developed. For purpose of constructing these models, 44 different mixes with 284 experimental data were gathered from the literature. The data used in the artificial neural networks and fuzzy logic models are arranged in a format of five input parameters that cover the age of specimen, Portland cement, ground granulated blast furnace slag, water and aggregate, and output parameter which is 3, 7, 14, 28, 63, 90, 119, 180 and 365-day compressive strength. In the models of the training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for prediction of long-term effects of ground granulated blast furnace slag oil compressive strength of concrete. (C) 2008 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.conbuildmat.2008.07.021
dc.identifier.endpage1286
dc.identifier.issn0950-0618
dc.identifier.issn1879-0526
dc.identifier.issue3
dc.identifier.scopus2-s2.0-57749180879
dc.identifier.scopusqualityQ1
dc.identifier.startpage1279
dc.identifier.urihttps://dx.doi.org/10.1016/j.conbuildmat.2008.07.021
dc.identifier.urihttps://hdl.handle.net/11480/5078
dc.identifier.volume23
dc.identifier.wosWOS:000262773000011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherELSEVIER SCI LTD
dc.relation.ispartofCONSTRUCTION AND BUILDING MATERIALS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCompressive strength
dc.subjectSlag
dc.subjectArtificial neural networks
dc.subjectFuzzy logic
dc.titlePrediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic
dc.typeArticle

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