Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4744
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dc.contributor.authorAkkurt, Sedat-
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorCan, Sever-
dc.date.accessioned2016-06-09T08:13:06Z
dc.date.available2016-06-09T08:13:06Z
dc.date.issued2004-08
dc.identifier.citationAkkurt, S., Tayfur, G., and Can, S. (2004). Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 34(8), 1429-1433. doi:10.1016/j.cemconres.2004.01.020en_US
dc.identifier.issn0008-8846
dc.identifier.issn0008-8846-
dc.identifier.urihttp://doi.org/10.1016/j.cemconres.2004.01.020
dc.identifier.urihttp://hdl.handle.net/11147/4744
dc.description.abstractA fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofCement and Concrete Researchen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectCompressive strengthen_US
dc.subjectFuzzy logicen_US
dc.subjectConcretesen_US
dc.subjectDefuzzificationen_US
dc.titleFuzzy logic model for the prediction of cement compressive strengthen_US
dc.typeArticleen_US
dc.authoridTR3591en_US
dc.authoridTR2054en_US
dc.institutionauthorAkkurt, Sedat-
dc.institutionauthorTayfur, Gökmen-
dc.institutionauthorCan, Sever-
dc.departmentIzmir Institute of Technology. Mechanical Engineeringen_US
dc.departmentIzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume34en_US
dc.identifier.issue8en_US
dc.identifier.startpage1429en_US
dc.identifier.endpage1433en_US
dc.identifier.wosWOS:000224017600016en_US
dc.identifier.scopus2-s2.0-3142763999en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.cemconres.2004.01.020-
dc.relation.doi10.1016/j.cemconres.2004.01.020en_US
dc.coverage.doi10.1016/j.cemconres.2004.01.020en_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept03.09. Department of Materials Science and Engineering-
crisitem.author.dept03.03. Department of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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