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

Basit Öğe Kaydı
dc.authorid0000-0002-2054-3323en_US
dc.contributor.authorKına, Ceren
dc.contributor.authorTurk, Kazım
dc.contributor.authorTanyildizi,Harun
dc.date.accessioned2022-03-21T13:09:46Z
dc.date.available2022-03-21T13:09:46Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractIn this work, to estimate the compressive, splitting tensile, and flexuralstrength of self-compacting concrete (SCC) having single fiber and binary, ter-nary, and quaternary fiber hybridization, the deep-learning (DL) and supportvector regression (SVR) models were devised. The fiber content and coarseaggregate/total aggregate ratio (CA/TA) were the variables for 24 designedmixtures. Four different fibers, which were a macro steel fiber, two types ofmicro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber,were used in SCC mixtures. The specimens of each mixture were tested to mea-sure the engineering properties for 7, 28, and 90 days. The amount of cement,fly ash, fine aggregate, CA, high-range water-reducing admixture, water, macrosteel fiber, PVA fiber, two types of micro steel fibers, and curing time wereselected as input layers while the output layers were strength results. Theexperimental results were compared with the estimation results. The engineer-ing properties were estimated using the SVR model with 95.25%, 87.81%, and93.89% accuracy, respectively. Furthermore, the DL model estimated compres-sive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and99.15% accuracy, respectively. It was found that the DL estimated the engineer-ing properties of hybrid fiber–reinforced SCC with higher accuracy than SVRen_US
dc.identifier.citationKina, C., Turk, K., & Tanyildizi, H. (2022). Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models. Structural Concrete.en_US
dc.identifier.doi10.1002/suco.202100622
dc.identifier.endpage18en_US
dc.identifier.scopus2-s2.0-85123893365en_US
dc.identifier.startpage1en_US
dc.identifier.uri10.1002/suco.202100622
dc.identifier.urihttps://hdl.handle.net/20.500.12899/743
dc.identifier.wosWOS:000749646600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKına, Ceren
dc.language.isoenen_US
dc.relation.echttps://doi.org/10.1002/suco.202100622
dc.relation.ispartofWILEYen_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 fibeen_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

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu