Açık Akademik Arşiv Sistemi

Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network

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dc.contributor.authors Uysal, M; Tanyildizi, H
dc.date.accessioned 2020-03-06T08:08:09Z
dc.date.available 2020-03-06T08:08:09Z
dc.date.issued 2011
dc.identifier.citation Uysal, M; Tanyildizi, H (2011). Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network. CONSTRUCTION AND BUILDING MATERIALS, 25, 4111-4105
dc.identifier.issn 0950-0618
dc.identifier.uri https://doi.org/10.1016/j.conbuildmat.2010.11.108
dc.identifier.uri https://hdl.handle.net/20.500.12619/67271
dc.description.abstract In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. This study is based on the determination of the variation of core compressive strength, water absorption and unit weight in curtain wall elements. One conventional concrete (vibrated concrete) and six different self-compacting concrete (SCC) mixtures with mineral additives were prepared. SCC mixtures were produced as control concrete (without mineral additives), moreover fly ash and limestone powder were used with two different replacement ratios (15% and 30%) of cement and marble powder was used with 15% replacement ratio of cement. SCC mixtures were compared to conventional concrete according to the variation of compressive strength, water absorption and unit weight. It can be seen from this study, self-compacting concretes consolidated by its own weight homogeneously in the narrow reinforcement construction elements. Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength. ANN model is constructed, trained and tested using these data. The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
dc.language English
dc.publisher ELSEVIER SCI LTD
dc.title Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network
dc.type Article
dc.identifier.volume 25
dc.identifier.startpage 4105
dc.identifier.endpage 4111
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü
dc.contributor.saüauthor Uysal, Mücteba
dc.contributor.saüauthor Uysal, Mehmet
dc.relation.journal CONSTRUCTION AND BUILDING MATERIALS
dc.identifier.wos WOS:000293319600002
dc.identifier.doi 10.1016/j.conbuildmat.2010.11.108
dc.contributor.author Uysal, Mücteba
dc.contributor.author Uysal, Mehmet


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