Extreme Learning Machine for Estimation of the Engineering Properties of Self-Compacting Mortar with High-Volume Mineral Admixtures

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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.authorwosidKina, Ceren/KRP-5310-2024
dc.authorwosidTANYILDIZI, Harun/A-1950-2016
dc.authorwosidKina, Ceren/KIE-5891-2024
dc.contributor.authorTurk, Kazim
dc.contributor.authorKina, Ceren
dc.contributor.authorTanyildizi, Harun
dc.date.accessioned2024-08-04T20:54:27Z
dc.date.available2024-08-04T20:54:27Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe utilization of supplementary cementitious materials obtained from industrial by-products or wastes is one of the most effective ways to minimize the costs as well as environmental impact associated with cement production. This work investigated the effects of the replacement of Portland cement (PC) with (25, 30, 35 and 40%) fly ash (FA) and (5, 10, 15, and 20%) silica fume (SF) by weight as binary and ternary blends on the compressive strength (f(c)) and flexural strength (f(ft)) of self-compacting mortars (SCMs) at 28 and 91 curing days. Extreme learning machine (ELM), support vector regression (SVR), artificial neural network (ANN), and decision tree (DT) models were devised to predict these strengths of SCMs containing high-volume mineral admixture (HVMA). The selected input variables were the number of curing days, water-cementitious material (W/CM), PC, FA, SF, and sand contents, while the f(c) and f(ft) were the output variables. ANOVA results show that the curing time was the most effective parameter for determining both strengths. The results also indicated that ELM achieved superior performance for the prediction of f(c) and f(ft) of SCMs with HVMA compared to SVR, ANN, and DT due to having the highest coefficient of determination values of 0.9802 for both strengths.en_US
dc.identifier.doi10.1007/s40996-023-01153-3
dc.identifier.endpage60en_US
dc.identifier.issn2228-6160
dc.identifier.issn2364-1843
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85161875423en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage41en_US
dc.identifier.urihttps://doi.org/10.1007/s40996-023-01153-3
dc.identifier.urihttps://hdl.handle.net/11616/101408
dc.identifier.volume48en_US
dc.identifier.wosWOS:001008423500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Int Publ Agen_US
dc.relation.ispartofIranian Journal of Science and Technology-Transactions of Civil Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFly ashen_US
dc.subjectSilica fumeen_US
dc.subjectStrengthsen_US
dc.subjectMachine learningen_US
dc.subjectANOVA analysisen_US
dc.titleExtreme Learning Machine for Estimation of the Engineering Properties of Self-Compacting Mortar with High-Volume Mineral Admixturesen_US
dc.typeArticleen_US

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