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Prediction of sulfate resistance of cements produced with GBFS and SS additives using artificial neural network

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dc.contributor.authors Ozkan, O; Yilmaz, C; Koubaa, A;
dc.date.accessioned 2020-01-13T12:15:20Z
dc.date.available 2020-01-13T12:15:20Z
dc.date.issued 2013
dc.identifier.citation Ozkan, O; Yilmaz, C; Koubaa, A; (2013). Prediction of sulfate resistance of cements produced with GBFS and SS additives using artificial neural network. INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY, 46, 231-215
dc.identifier.issn 0268-1900
dc.identifier.uri https://hdl.handle.net/20.500.12619/2856
dc.description.abstract Concrete structures built on sulfate rich soil or wetland, or directly exposed to seawater are subjected to sulfate attack, which might be critical, as the durability of concrete is highly dependent on its resistance against sulfate compounds. The objective of this study is to develop a methodology for the prediction sulfate resistance capabilities of sulfate resistance of mortars prepared with cements incorporating granulated blast-furnace slag (GBFS) and steel slag (SS) as partial replacement of Portland cement clinker in different ratios. Three different combinations of GBFS and SS were utilised to partially replace Portland cement clinker at various proportions from 20% to 80%. Parameters such as specific surface, specific gravity, volumetric expansion, Vicat setting time, compressive strength, sulfate resistance and durability against high temperature were investigated on the produced cement samples. Furthermore, experimental results were also obtained by building models in accordance with the artificial neural network (ANN) technique to predict the sulfate resistance of cements. The results showed that ANNs can be successfully used to model the relationship between the sulfate resistance and each of the observed parameters.
dc.description.uri https://doi.org/10.1504/IJMPT.2013.058930
dc.language English
dc.publisher INDERSCIENCE ENTERPRISES LTD
dc.subject Materials Science
dc.title Prediction of sulfate resistance of cements produced with GBFS and SS additives using artificial neural network
dc.type Article
dc.identifier.volume 46
dc.identifier.startpage 215
dc.identifier.endpage 231
dc.contributor.department Sakarya Üniversitesi/Teknik Eğitim Fakültesi/Yapı Eğitimi Bölümü
dc.contributor.saüauthor Özkan, Ömer
dc.contributor.saüauthor Yılmaz, Cemal
dc.relation.journal INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY
dc.identifier.wos WOS:000330611500001
dc.identifier.doi 10.1504/IJMPT.2013.058930
dc.identifier.eissn 1741-5209
dc.contributor.author Özkan, Ömer
dc.contributor.author Yılmaz, Cemal


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