Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

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dc.authorid0000-0003-2213-6155
dc.contributor.authorAtici, U.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2011
dc.departmentNiğde ÖHÜ
dc.description.abstractThis study applies multiple regression analysis and an artificial neural network in estimating the compressive strength of concrete that contains various amounts of blast furnace slag and fly ash, based on the properties of the additives (blast furnace slag and fly ash in this case) and values obtained by non-destructive testing rebound number and ultrasonic pulse velocity for 28 different concrete mixtures (M(control) and M(1)-M(27)) at different curing times (3, 7, 28, 90, and 180 days). The results obtained using the two methods are then compared and discussed. The results reveal that although multiple regression analysis was more accurate than artificial neural network in predicting the compressive strength using values obtained from non-destructive testing, the artificial neural network models performed better than did multiple regression analysis models. The application of an artificial neural network to the prediction of the compressive strength in admixture concrete of various curing times shows great potential in terms of inverse problems, and it is suitable for calculating nonlinear functional relationships, for which classical methods cannot be applied. (C) 2011 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2011.01.156
dc.identifier.endpage9618
dc.identifier.issn0957-4174
dc.identifier.issue8
dc.identifier.scopus2-s2.0-79953730262
dc.identifier.scopusqualityQ1
dc.identifier.startpage9609
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2011.01.156
dc.identifier.urihttps://hdl.handle.net/11480/4703
dc.identifier.volume38
dc.identifier.wosWOS:000290237500064
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAtici, U.
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAdmixture concrete
dc.subjectCompressive strength
dc.subjectMultiple regression analysis
dc.subjectArtificial neural network
dc.titlePrediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network
dc.typeArticle

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