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Prediction of temperature distribution in high-strength concrete using hydration model

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dc.contributor.authorPark, Ki-Bong-
dc.contributor.authorJee, Nam-Yong-
dc.contributor.authorYoon, In-Seok-
dc.contributor.authorLee, Han-Seung-
dc.date.accessioned2021-06-23T18:02:47Z-
dc.date.available2021-06-23T18:02:47Z-
dc.date.issued2008-03-
dc.identifier.issn0889-325X-
dc.identifier.issn1944-737X-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/42628-
dc.description.abstractThis paper presents the development of a computational program to predict the temperature history in high-strength concrete members. The numerical simulation procedure starts with a hydration model that describes the evolution of cement paste microstructure,as a function of the changing composition of the hydration products. The coefficients for the hydration model were determined with an artificial neural network technique. Temperature distribution, and history in concrete members considering thermal conductivity and radiant heat were calculated based on a three-dimensional mesh. Predicted temperature history curves were compared with experimental data and a good correlation was found.-
dc.format.extent7-
dc.language영어-
dc.language.isoENG-
dc.publisherAMER CONCRETE INST-
dc.titlePrediction of temperature distribution in high-strength concrete using hydration model-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.scopusid2-s2.0-42949101439-
dc.identifier.wosid000255049600009-
dc.identifier.bibliographicCitationACI MATERIALS JOURNAL, v.105, no.2, pp 180 - 186-
dc.citation.titleACI MATERIALS JOURNAL-
dc.citation.volume105-
dc.citation.number2-
dc.citation.startPage180-
dc.citation.endPage186-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaConstruction & Building Technology-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryConstruction & Building Technology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordAuthorhigh-strength concrete-
dc.subject.keywordAuthorhydration model-
dc.subject.keywordAuthorneural network-
dc.identifier.urlhttps://www.proquest.com/docview/197937710?accountid=11283-
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ERICA 공학대학 (MAJOR IN ARCHITECTURAL ENGINEERING)
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