Analyzing the compressive strength of clinker mortars using approximate reasoning approaches - ANN vs MLR

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dc.contributor.authorBeycioğlu, Ahmet
dc.contributor.authorEmiroğlu, Mehmet
dc.contributor.authorKoçak, Yılmaz
dc.contributor.authorSubaşı, Serkan
dc.date.accessioned2020-04-30T22:39:23Z
dc.date.available2020-04-30T22:39:23Z
dc.date.issued2015
dc.departmentDÜ, Teknoloji Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.descriptionEmiroglu, Mehmet/0000-0002-0214-4986en_US
dc.descriptionWOS: 000352071900006en_US
dc.description.abstractIn this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate (C3S), dicalcium silicate (C2S), tricalcium aluminate (C(3)A), tetracalcium alumina ferrite (C(4)AF), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.en_US
dc.identifier.doi10.12989/cac.2015.15.1.089
dc.identifier.endpage101en_US
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage89en_US
dc.identifier.urihttps://doi.org/10.12989/cac.2015.15.1.089
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2707
dc.identifier.volume15en_US
dc.identifier.wosWOS:000352071900006en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTechno-Pressen_US
dc.relation.ispartofComputers And Concreteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectclinkeren_US
dc.subjectpredictionen_US
dc.subjectcompressive strengthen_US
dc.subjectartificial neural networksen_US
dc.subjectmulti linear regressionen_US
dc.titleAnalyzing the compressive strength of clinker mortars using approximate reasoning approaches - ANN vs MLRen_US
dc.typeArticleen_US

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