A linguistic model for evaluating cement strength

dc.authoridDag, Ahmet/0000-0003-4628-5067
dc.authoridTutmez, Bulent/0000-0002-2618-3285
dc.authorwosidTutmez, Bulent/ABG-8630-2020
dc.authorwosidDag, Ahmet/B-4046-2008
dc.contributor.authorTutmez, B.
dc.contributor.authorDag, A.
dc.date.accessioned2024-08-04T20:31:05Z
dc.date.available2024-08-04T20:31:05Z
dc.date.issued2009
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper presents a soft methodology for predicting the 28-day compressive strength of Portland cement (CCS) by making use of the 1-day, 3-day and 7-day CCS values. Data taken from a cement plant in Turkey have been employed in the model construction and testing. For implementation, linguistic models were designed based on if-then fuzzy rules. In addition, predictions of these models were compared with results of the regression models. The performance evaluations showed that the linguistic-based fuzzy predictions are very satisfactory in estimating cement strength and the linguistic modeling performs better than regression modeling.en_US
dc.identifier.doi10.1617/s11527-008-9370-1
dc.identifier.endpage111en_US
dc.identifier.issn1359-5997
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-56749104026en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage103en_US
dc.identifier.urihttps://doi.org/10.1617/s11527-008-9370-1
dc.identifier.urihttps://hdl.handle.net/11616/94704
dc.identifier.volume42en_US
dc.identifier.wosWOS:000262988200009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMaterials and Structuresen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectModelingen_US
dc.subjectCompressive strengthen_US
dc.subjectLinguistic approachen_US
dc.subjectCementen_US
dc.titleA linguistic model for evaluating cement strengthen_US
dc.typeArticleen_US

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