A data-driven study for evaluating fineness of cement by various predictors

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dc.authoridTutmez, Bulent/0000-0002-2618-3285
dc.authorwosidTutmez, Bulent/ABG-8630-2020
dc.contributor.authorTutmez, Bulent
dc.date.accessioned2024-08-04T20:40:12Z
dc.date.available2024-08-04T20:40:12Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description.abstractModelling relationships among cement and concrete parameters from different perspectives is preferred due to its practical importance. The relationship between chemical ingredients and specific surface area which addresses fineness of cement were appraised via three predictors: robust regression (RR), support vector regression (SVR) and multi-layer perception (MLP). The main motivation of the study was to give a comparative assessment with sparse data based on accuracy of the models. In addition to accuracy, smoothing level of the estimations was also considered and the performances of three models were compared with the former practices. The experimental studies showed that the SVR model performs better than the rest of the models for identifying the relationships. The potentials of the MLP and the RR models have also been discussed.en_US
dc.identifier.doi10.1007/s13042-014-0280-y
dc.identifier.endpage510en_US
dc.identifier.issn1868-8071
dc.identifier.issn1868-808X
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84929073715en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage501en_US
dc.identifier.urihttps://doi.org/10.1007/s13042-014-0280-y
dc.identifier.urihttps://hdl.handle.net/11616/96775
dc.identifier.volume6en_US
dc.identifier.wosWOS:000354391500015en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofInternational Journal of Machine Learning and Cyberneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFineness of cementen_US
dc.subjectRegressionen_US
dc.subjectSupport vector machineen_US
dc.subjectNeural networken_US
dc.titleA data-driven study for evaluating fineness of cement by various predictorsen_US
dc.typeArticleen_US

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