An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

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dc.authorid0000-0002-9070-7258en_US
dc.authorid0000-0001-9297-8134en_US
dc.authorid0000-0001-7862-6183en_US
dc.authorid0000-0001-7416-4428en_US
dc.authorid0000-0002-7111-5767en_US
dc.contributor.authorAdamu, Musa
dc.contributor.authorÇolak, A. Batur
dc.contributor.authorUmar, Ibraim K.
dc.contributor.authorIbrahim, Yasser E.
dc.contributor.authorHamza, Mukhtar F.
dc.date.accessioned2024-03-25T12:40:03Z
dc.date.available2024-03-25T12:40:03Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractPlastic waste (PW) is a major soild waste, which its generation continues to increase globally year in and year out. Proper management of the PW is still a challenge due to its non-biodegradable nature. One of the most convenient ways of managing plastic waste is by using it in concrete as a partial substitute for natural aggregate. However, the main shortcomings of adding plastic waste to concrete are a reduction in strength and durability. Hence, to reduce the undesirable impact of the PW in concrete, highly reactive additives are normally added. In this research, 240 experimental datasets were used to train an artificial neural network (ANN) model using Levenberg Marquadt algorithms for the prediction of the mechanical properties and durability of high-volume fly ash (HVFA) concrete containing fly ash and PW as partial substitutes for cement and coarse aggregate, respectively, and graphene nanoplatlets (GNP) as additives to cementitious materials. The optimized model structure has five input parameters, 17 hidden neurons, and one output layer for each of the physical parameters. The results were analyzed graphically and statistically. The obtained results revealed that the generated network model can forecast with deviations less than 0.48%. The efficiency of the ANN model in predicting concrete properties was compared with that of the SVR (support vector regression) and SWLR (stepwise regression) models. The ANN outperformed SVR and SWLR for all the models by up to 6% and 74% for SVR and SWLR, respectively, in the confirmation stage. The graphical analysis of the results further demonstrates the higher prediction ability of the ANN.en_US
dc.identifier.doi10.28991/CEJ-2023-09-09-04en_US
dc.identifier.endpage2160en_US
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85174288057en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage2145en_US
dc.identifier.urihttps://hdl.handle.net/11467/7184
dc.identifier.urihttps://doi.org/10.28991/CEJ-2023-09-09-04
dc.identifier.volume9en_US
dc.identifier.wosWOS:001101893600004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCEJ PUBLISHING GROUPen_US
dc.relation.ispartofCivil Engineering Journal-Tehranen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPlastic Waste; Fly Ash; Graphene Nanoplatelets (GNP); ANN; SVM; SWLRen_US
dc.titleAn Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concreteen_US
dc.typeArticleen_US

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