Prediction of compressive strength of heavyweight concrete by ANN and FL models

dc.contributor.authorBaşyiğit, Celalettin
dc.contributor.authorAkkurt, İskender
dc.contributor.authorKılınçarslan, Şemsettin
dc.contributor.authorBeycioğlu, Ahmet
dc.date.accessioned2020-04-30T23:21:15Z
dc.date.available2020-04-30T23:21:15Z
dc.date.issued2010
dc.departmentDÜ, Teknik Eğitim Fakültesi, Yapı Eğitimi Bölümüen_US
dc.descriptionAkkurt, Iskender/0000-0002-5247-7850; Kilincarslan, Semsettin/0000-0001-8253-9357en_US
dc.descriptionWOS: 000277940600001en_US
dc.description.abstractThe compressive strength of heavyweight concrete which is produced using baryte aggregates has been predicted by artificial neural network (ANN) and fuzzy logic (FL) models. For these models 45 experimental results were used and trained. Cement rate, water rate, periods (7-28-90 days) and baryte (BaSO(4)) rate (%) were used as inputs and compressive strength (MPa) was used as output while developing both ANN and FL models. In the models, training and testing results have shown that ANN and FL systems have strong potential for predicting compressive strength of concretes containing baryte (BaSO(4)).en_US
dc.identifier.doi10.1007/s00521-009-0292-9en_US
dc.identifier.endpage513en_US
dc.identifier.issn0941-0643
dc.identifier.issue4en_US
dc.identifier.startpage507en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-009-0292-9
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4161
dc.identifier.volume19en_US
dc.identifier.wosWOS:000277940600001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHeavyweight concreteen_US
dc.subjectBaryteen_US
dc.subjectCompressive strengthen_US
dc.subjectArtificial neural networksen_US
dc.subjectFuzzy logicen_US
dc.subjectComputer simulationen_US
dc.titlePrediction of compressive strength of heavyweight concrete by ANN and FL modelsen_US
dc.typeArticleen_US

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