Marshall Stability Estimating Using Artificial Neural Network with Polyparaphenylene Terephtalamide Fibre Rate

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dc.contributor.authorKarahançer, Şebnem
dc.contributor.authorÇapalı, Buket
dc.contributor.authorErişkin, Ekinhan
dc.contributor.authorMorova, Nihat
dc.contributor.authorSerin, Sercan
dc.contributor.authorSaltan, Mehmet
dc.contributor.authorKüçükçapraz, Dicle Özdemir
dc.date.accessioned2020-04-30T23:19:04Z
dc.date.available2020-04-30T23:19:04Z
dc.date.issued2016
dc.departmentDÜ, Teknoloji Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.descriptionInternational Symposium on Innovations in Intelligent Systems and Applications (INISTA) -- AUG 02-05, 2016 -- Sinaia, ROMANIAen_US
dc.descriptionSaltan, Mehmet/0000-0001-6221-4918; terzi, serdal/0000-0002-4776-824X; Eriskin, Ekinhan/0000-0002-0087-0933; Eriskin, Ekinhan/0000-0002-0087-0933; Karahancer, Sebnem/0000-0001-7734-2365en_US
dc.descriptionWOS: 000386824000029en_US
dc.description.abstractDue to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting stability of asphalt pavement is difficult. To predict, it is required to find the mathematical relation between the input and output data by an accurate and simple method. In recent years, artificial neural networks (ANNs) have been used to model the properties and behaviour of materials, and to find complex relations between different properties in many fields of civil engineering applications, because of their ability to learn and to adapt. In the present study, laboratory data are obtained from an experimental study that was used to develop an ANN model. For predicting the Marshall Stability value of mixture using ANN models, an appropriate selection of input parameters (neurons) is essential. There are four nodes in the input layer corresponding to four variables: Polyparaphenylene Terephtalamide fibre (PTF) rate, binder rate, flow, volume of the specimen. The result indicates that the proposed model can be applied in predicting Marshall Stability of asphalt mixtures. The model is further applied to evaluate the effect of different rates of Polyparaphenylene Terephtalamide on Marshall Stability.en_US
dc.description.sponsorshipIEEE, IDS Res Grp, Fac Automat Comp & Elect, Dept Comp & Informat Technol, Fac Econ & Business Adm, Dept Stat & Business Informat, Fac Math & Nat Sci, Dept Informat, Univ Craiovaen_US
dc.identifier.isbn978-1-4673-9910-4
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3639
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartofProceedings Of The 2016 International Symposium On Innovations In Intelligent Systems And Applications (Inista)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural network modelen_US
dc.subjectpolyparaphenylene terephtalamideen_US
dc.subjectmarshall stabilityen_US
dc.titleMarshall Stability Estimating Using Artificial Neural Network with Polyparaphenylene Terephtalamide Fibre Rateen_US
dc.typeConference Objecten_US

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