Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC

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dc.authorid0000-0002-2054-3323en_US
dc.contributor.authorKına‬, ‪Ceren
dc.contributor.authorTürk, Kazım
dc.contributor.authorAtalay, Esma
dc.contributor.authorDönmez, İzzeddin
dc.contributor.authorTanyıldızı, Harun
dc.date.accessioned2021-09-29T07:55:23Z
dc.date.available2021-09-29T07:55:23Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description(Early Access)en_US
dc.description.abstractThis paper studied the estimation of fresh properties of hybrid fiber-reinforced self-compacting concrete (HR-SCC) mixtures with different types and combinations of fibers by using two different prediction method named as the methodologies of extreme learning machine and long short-term memory (LSTM). For this purpose, 48 mixtures, which were designed as single, binary, ternary and quaternary fiber-reinforced SCC with macro-steel fiber, two micro-steel fibers having different aspect ratio, polypropylene (PP) and polyvinylalcohol (PVA), were used. Slump flow, t50 and J-ring tests for designed mixtures were conducted to measure the fresh properties of fiber-reinforced SCC mixtures as per EFNARC. The experimental results were analyzed by Anova method. In the devised prediction model, the amounts of cement, fly ash, silica fume, blast furnace slag, limestone powder, aggregate, water, high-range water-reducer admixture (HRWA) and the fiber ratios were selected as inputs, while the slump flow, t50 and the J-ring were selected as outputs. Based on the Anova analysis’ results, the macro-steel fiber was the most important parameter for the results of slump-flow diameter and t50, while the most important parameter for the results of J-ring was fly ash. Furthermore, it was found that the use of more than 0.20% by volume of 6/0.16 micro-steel fiber positively influenced the fresh properties of SCC mixtures with hybrid fiber. On the other hand, the inclusion of steel fiber instead of synthetic fiber into SCC mixture as micro-fiber was more advantageous in terms of workability of mixtures as result of hydrophobic nature of steel fibers. This study found that extreme learning machine model estimated the slump flow, t50 and J-ring with 99.71%, 81% and 94.21% accuracy, respectively, while deep learning model found the same experimental results with 99.18%, 77.4% and 84.8% accuracy, respectively. It can be emphasized from this study that the extreme learning machine model had a better prediction ability than the deep learning model.en_US
dc.identifier.citationKina, C., Turk, K., Atalay, E., Donmez, I., & Tanyildizi, H. (2021). Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC. Neural Computing and Applications, 1-19.en_US
dc.identifier.doi10.1007/s00521-021-05836-8
dc.identifier.endpage11659en_US
dc.identifier.issue18en_US
dc.identifier.scopus2-s2.0-85102502557en_US
dc.identifier.startpage11641en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-05836-8
dc.identifier.urihttps://hdl.handle.net/20.500.12899/445
dc.identifier.volume33en_US
dc.identifier.wosWOS:000627702600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKına, Ceren
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHybrid fiber-reinforced SCCen_US
dc.subjectWorkabilityen_US
dc.subjectAnova analysisen_US
dc.subjectExtreme learning machineen_US
dc.subjectDeep learningen_US
dc.titleComparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCCen_US
dc.typeArticleen_US

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