Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete

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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/KRP-5310-2024
dc.authorwosidTANYILDIZI, Harun/A-1950-2016
dc.authorwosidKina, Ceren/KIE-5891-2024
dc.authorwosidTurk, Kazim/AAB-7513-2019
dc.contributor.authorKina, Ceren
dc.contributor.authorTurk, Kazim
dc.contributor.authorTanyildizi, Harun
dc.date.accessioned2024-08-04T20:51:41Z
dc.date.available2024-08-04T20:51:41Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDeep auto-encoders and long short-term memory methodology (LSTM) based on deep learning as well as support vector regression (SVR) and k-nearest neighbors (kNN) based on machine learning models for the capillary water absorption prediction of self-compacting concrete (SCC) with single and binary, ternary, and quaternary fiber hybridization were developed. A macro and two types of micro steel fibers having different aspect ratios, and PVA fiber were used. One hundred and sixty-eight specimens produced from 24 mixtures were used in the prediction models. The input was the content of cement, fly ash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macro and micro steel fibers, PVA, time that the specimen was immersed in water, and splitting tensile strength. Water absorption was used as output. As per the ANOVA analysis of the experiment results, the most effective parameters were macro steel fiber and time for tensile strength and water absorption, respectively. Finally, binary hybridization of 1% macro steel fiber and PVA improved the splitting tensile strength while the use of PVA as binary, ternary, and quaternary fiber hybridization increased the water absorption of SCC specimens. The auto-encoder, LSTM, SVR, and kNN models predicted the water absorption of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy, respectively. The performance of deep autoencoder in the estimation of water absorption of fiber-reinforced SCC was superior to the other prediction models.en_US
dc.description.sponsorshipScientific Research Projects Committee of Inonu University, Turkey [FDK-2017-865]en_US
dc.description.sponsorshipScientific Research Projects Committee of Inonu University, Turkey, Grant/Award Number: FDK-2017-865en_US
dc.identifier.doi10.1002/suco.202100756
dc.identifier.endpage3358en_US
dc.identifier.issn1464-4177
dc.identifier.issn1751-7648
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85124715523en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3331en_US
dc.identifier.urihttps://doi.org/10.1002/suco.202100756
dc.identifier.urihttps://hdl.handle.net/11616/100488
dc.identifier.volume23en_US
dc.identifier.wosWOS:000755714300001en_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.subjectANOVAen_US
dc.subjectcapillary water absorptionen_US
dc.subjectdeep learningen_US
dc.subjecthybrid fiber reinforced SCCen_US
dc.subjectmachine learningen_US
dc.titleDeep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concreteen_US
dc.typeArticleen_US

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