Effect of expanded perlite aggregate on cyclic thermal loading of HSC and artificial neural network modeling

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dc.authoridTurkmen, Ibrahim/0000-0001-7560-0535
dc.authoridKARAKOÇ, MEHMET BURHAN/0000-0002-6954-0051
dc.authorwosidTurkmen, Ibrahim/AAH-1541-2019
dc.authorwosidKARAKOÇ, MEHMET BURHAN/ABG-5446-2020
dc.contributor.authorKarakoc, M. B.
dc.contributor.authorDemirboga, R.
dc.contributor.authorTurkmen, I.
dc.contributor.authorCan, I.
dc.date.accessioned2024-08-04T20:35:44Z
dc.date.available2024-08-04T20:35:44Z
dc.date.issued2012
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper describes a laboratory investigation of the resistance to freezing and thawing of Expanded Perlite Aggregate (EPA) concrete, compared with that of natural aggregate concrete. The effects of EPA ratios on High Strength Concrete (HSC) properties were studied for 28 days. EPA replacements of fine aggregate (0-2 mm) were used: 10%, 20% and 30%. The properties examined included compressive strength, Ultrasound Pulse Velocity (UPV), porosity, microstructure and the Relative Dynamic Modulus of Elasticity (RDME) of HSC. Results showed that the compressive strength, UPV and RDME of samples were decreased with an increase in EPA ratios. Test results revealed that HSC was still durable after 100, 200 and 300 cycles of freezing and thawing in accordance with the ASTM C666. After 300 cycles, reduction in compressive strength and RDME ranged from 7% to 29% and 5% to 21%, respectively. In this paper, feed-forward Artificial Neural Network (ANNs) techniques were used to model the relative change in compressive strength and UPV in cyclic thermal loading. Genetic algorithms were applied in order to determine optimum mix proportions subjected to 300 thermal cycling. The best performance was obtained from HSC with about 10% EPA. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipSciences, Technology and Research Council of Turkey (TUBITAK) [106M014]; Sciences, Technology and Research Council of Turkey (TUBITAK) [106M014]en_US
dc.description.sponsorshipThe authors are grateful to the Sciences, Technology and Research Council of Turkey (TUBITAK) for their financial support for this project (106M014).en_US
dc.identifier.doi10.1016/j.scient.2011.11.035
dc.identifier.endpage50en_US
dc.identifier.issn1026-3098
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-84856900129en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage41en_US
dc.identifier.urihttps://doi.org/10.1016/j.scient.2011.11.035
dc.identifier.urihttps://hdl.handle.net/11616/95563
dc.identifier.volume19en_US
dc.identifier.wosWOS:000302388800005en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofScientia Iranicaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFreezing-thawingen_US
dc.subjectExpanded perlite aggregateen_US
dc.subjectHigh strength concreteen_US
dc.subjectCompressive strengthen_US
dc.subjectRelative dynamic modulus of elasticityen_US
dc.subjectArtificial neural networken_US
dc.titleEffect of expanded perlite aggregate on cyclic thermal loading of HSC and artificial neural network modelingen_US
dc.typeArticleen_US

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