Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network

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dc.authorid0000-0002-7585-2609
dc.authorscopusid36730697100
dc.authorscopusid38362627300
dc.authorscopusid23480721400
dc.authorscopusid6701781550
dc.authorwosidUysal, Mucteba/AAZ-5784-2020
dc.authorwosidTANYILDIZI, Harun/A-1950-2016
dc.contributor.authorAkyüncü, Veysel
dc.contributor.authorUysal, Mücteba
dc.contributor.authorTanyıldızı, Harun
dc.contributor.authorSümer, Mansur
dc.date.accessioned2022-05-11T14:26:39Z
dc.date.available2022-05-11T14:26:39Z
dc.date.issued2018
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractThis paper presents the modeling of an experimental investigation carried out to evaluate some mechanical and durability properties of concrete mixtures in which cement was partially replaced with Class C and Class F fly ash. A total of 39 mixtures with different mix designs were prepared. After compressive strength testing, the mixtures containing Class F and Class C fly ashes which had similar compressive strength values to control mixtures at 28 d for each series were used for sulfate resistance tests. The degree of sulfate attack was evaluated using expansion and weight loss. The test results indicated that Class C fly ash showed higher compressive strength than Class F fly ash. Moreover, the addition of fly ash significantly increased the resistance to sulfate attack when each amount of fly ash addition regardless of fly ash types was employed. In this paper, the Artificial Neural Network (ANNs) techniques were used to model the relative change in the weight and length of the concrete exposed to sulfate. The best algorithm for length changes of concrete exposed to sulfate is BFGS quasi-Newton backpropagation algorithm while the best algorithm for weight changes of concrete exposed to sulfate is the Levenberg-Marquardt backpropagation algorithm.
dc.identifier.doi10.7764/RDLC.17.3.337
dc.identifier.endpage353
dc.identifier.issn0718-915X
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85064910633
dc.identifier.scopusqualityN/A
dc.identifier.startpage337
dc.identifier.urihttps://doi.org/10.7764/RDLC.17.3.337
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6534
dc.identifier.volume17
dc.identifier.wosWOS:000457475200001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAkyüncü, Veysel
dc.language.isoen
dc.publisherPontificia Univ Catolica Chile, Escuela Construccion Civil
dc.relation.ispartofRevista De La Construccion
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClass F fly ash
dc.subjectClass C fly ash
dc.subjectthe weight change
dc.subjectthe length change
dc.subjectsulfate resistance
dc.subjectPortland-Cement Paste
dc.subjectFly-Ash
dc.subjectCompressive Strength
dc.subjectSilica Fume
dc.subjectMechanical-Properties
dc.subjectLightweight Mortar
dc.subjectPrediction
dc.subjectAttack
dc.subjectResistance
dc.subjectDurability
dc.titleModeling the weight and length changes of the concrete exposed to sulfate using artificial neural network
dc.title.alternativeModelización de los cambios de peso y longitud del concreto expuesto al sulfato mediante red neural artificial]
dc.typeArticle

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