Estimation of specific gravity with penetration and penetration index parameters by artificial neural network

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dc.contributor.authorSerin, Sercan
dc.contributor.authorKarahançer, Şebnem
dc.contributor.authorErişkin, Ekinhan
dc.contributor.authorMorova, Nihat
dc.contributor.authorSaltan, Mehmet
dc.contributor.authorTerzi, Serdal
dc.date.accessioned2020-04-30T13:32:26Z
dc.date.available2020-04-30T13:32:26Z
dc.date.issued2017
dc.departmentDÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractSpecific Gravity of the bitumen changes according to the ambient temperature. Different specific gravity values can be calculated at different temperature. Estimating models like Artificial Neural Network - ANN could be very useful to obtain the specific gravity value uniform. Specific gravity values obtained from Long-Term Pavement Performance - LTPP were estimated with artificial neural networks. Penetration and Penetration Index of binder were used for estimating the specific gravity of the bitumen. As a result, ANN get 84% of R2 between obtained and estimated values.en_US
dc.identifier.doi10.21533/pen.v5i2.106en_US
dc.identifier.endpage164en_US
dc.identifier.issn2303-4521
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage161en_US
dc.identifier.urihttps://dx.doi.org/10.21533/pen.v5i2.106
dc.identifier.urihttps://hdl.handle.net/20.500.12684/291
dc.identifier.volume5en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational University of Sarajevoen_US
dc.relation.ispartofPeriodicals of Engineering and Natural Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtifical neural network; Penetration; Penetration index; Specific gravityen_US
dc.titleEstimation of specific gravity with penetration and penetration index parameters by artificial neural networken_US
dc.typeArticleen_US

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