Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models

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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.authorKina, Ceren
dc.contributor.authorTurk, Kazim
dc.contributor.authorTanyildizi, Harun
dc.date.accessioned2024-08-04T20:51:37Z
dc.date.available2024-08-04T20:51:37Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this work, to estimate the compressive, splitting tensile, and flexural strength of self-compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep-learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high-range water-reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber-reinforced SCC with higher accuracy than SVR.en_US
dc.description.sponsorshipInonu Universitesi [FDK-2017-865]en_US
dc.description.sponsorshipInonu Universitesi, Grant/Award Number: FDK-2017-865en_US
dc.identifier.doi10.1002/suco.202100622
dc.identifier.endpage3330en_US
dc.identifier.issn1464-4177
dc.identifier.issn1751-7648
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85123893365en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3313en_US
dc.identifier.urihttps://doi.org/10.1002/suco.202100622
dc.identifier.urihttps://hdl.handle.net/11616/100451
dc.identifier.volume23en_US
dc.identifier.wosWOS:000749646600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherErnst & Sohnen_US
dc.relation.ispartofStructural Concreteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectestimationen_US
dc.subjecthybrid fiberen_US
dc.subjectstrengthen_US
dc.subjectsupport vector regressionen_US
dc.titleEstimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression modelsen_US
dc.typeArticleen_US

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