Predicting sorptivity and freeze-thaw resistance of self-compacting mortar by using deep learning and k-nearest neighbor

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dc.authoridKina, Ceren/0000-0002-2054-3323
dc.authoridTANYILDIZI, Harun/0000-0002-7585-2609
dc.authoridKina, Ceren/0000-0002-2054-3323
dc.authoridTurk, Kazim/0000-0002-6314-9465
dc.authorwosidKina, Ceren/KIE-5891-2024
dc.authorwosidTANYILDIZI, Harun/A-1950-2016
dc.authorwosidKina, Ceren/KRP-5310-2024
dc.authorwosidTurk, Kazim/AAB-7513-2019
dc.contributor.authorTurk, Kazi
dc.contributor.authorKina, Ceren
dc.contributor.authorTanyildizi, Harun
dc.date.accessioned2024-08-04T20:53:12Z
dc.date.available2024-08-04T20:53:12Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze -thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.en_US
dc.identifier.doi10.12989/cac.2022.30.2.099
dc.identifier.endpage111en_US
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85142657990en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage99en_US
dc.identifier.urihttps://doi.org/10.12989/cac.2022.30.2.099
dc.identifier.urihttps://hdl.handle.net/11616/101019
dc.identifier.volume30en_US
dc.identifier.wosWOS:000861288800002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTechno-Pressen_US
dc.relation.ispartofComputers and Concreteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectdurability factoren_US
dc.subjectk-nearest neighboren_US
dc.subjectpredictionen_US
dc.subjectself-compacting mortaren_US
dc.subjectsorptivity coefficienten_US
dc.titlePredicting sorptivity and freeze-thaw resistance of self-compacting mortar by using deep learning and k-nearest neighboren_US
dc.typeArticleen_US

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