Prediction of ratio of mineral substitution in the production of low-clinker factored cement by artificial neural network

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dc.contributor.authorCanpolat F.
dc.contributor.authorYilmaz K.
dc.contributor.authorAta R.
dc.contributor.authorKöse M.M.
dc.date.accessioned2024-07-22T08:24:57Z
dc.date.available2024-07-22T08:24:57Z
dc.date.issued2003
dc.description.abstractArtificial Neural Networks (ANN) has been widely used to solve some of the problems in science and engineering, which requires experimental analysis. Use of ANN in civil engineering applications started in late eighties. One of the important features of the ANN is its ability to learn from experience and examples and then to adapt with changing situations. Engineers often deal with incomplete and noisy data, which is one of the areas where ANN can easily be applied. Dealing with incomplete and noisy data is the conceptual stage of the design process. This paper shows practical guidelines for designing ANN for civil engineering applications. ANN is in cement industry: in the production of low-clinker factored cement, and in the derivation of composition of natural and artificial puzzolans in the production of high performance cement and concrete. By using ANN, a study to find out the optimum ratio of substitution and compression strengths was carried out.
dc.identifier.DOI-ID10.3390/mca8020209
dc.identifier.issn1300686X
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/20194
dc.language.isoEnglish
dc.publisherAssociation for Scientific Research
dc.rightsAll Open Access; Gold Open Access
dc.subjectCements
dc.subjectCivil engineering
dc.subjectComposition
dc.subjectCompressive strength
dc.subjectFly ash
dc.subjectMinerals
dc.subjectZeolites
dc.subjectArtificial puzzolans
dc.subjectNeural networks
dc.titlePrediction of ratio of mineral substitution in the production of low-clinker factored cement by artificial neural network
dc.typeConference paper

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