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

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dc.authorid0000-0002-2054-3323en_US
dc.contributor.authorKına, Ceren
dc.contributor.authorTurk, Kazım
dc.contributor.authorTanyildizi,Harun
dc.date.accessioned2022-03-22T07:41:24Z
dc.date.available2022-03-22T07:41:24Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractDeep auto-encoders and long short-term memory methodology (LSTM) basedon deep learning as well as support vector regression (SVR) and k-nearestneighbors (kNN) based on machine learning models for the capillary waterabsorption prediction of self-compacting concrete (SCC) with single andbinary, ternary, and quaternary fiber hybridization were developed. A macroand two types of micro steel fibers having different aspect ratios, and PVA fiberwere used. One hundred and sixty-eight specimens produced from 24 mixtureswere used in the prediction models. The input was the content of cement, flyash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macroand micro steel fibers, PVA, time that the specimen was immersed in water,and splitting tensile strength. Water absorption was used as output. As per theANOVA analysis of the experiment results, the most effective parameters weremacro steel fiber and time for tensile strength and water absorption, respec-tively. Finally, binary hybridization of 1% macro steel fiber and PVA improvedthe splitting tensile strength while the use of PVA as binary, ternary, and qua-ternary fiber hybridization increased the water absorption of SCC specimens.The auto-encoder, LSTM, SVR, and kNN models predicted the water absorp-tion 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 waterabsorption of fiber-reinforced SCC was superior to the other predictionmodels.en_US
dc.identifier.citationKina, C., Turk, K., & Tanyildizi, H. Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete. Structural Concrete.en_US
dc.identifier.doi10.1002/suco.202100756
dc.identifier.endpage28en_US
dc.identifier.scopus2-s2.0-85124715523en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1002/suco.202100756
dc.identifier.urihttps://hdl.handle.net/20.500.12899/745
dc.identifier.wosWOS:000755714300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKına, Ceren
dc.language.isoenen_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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