Estimation of compaction parameters of fine-grained soils in terms of compaction energy using artificial neural networks

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dc.contributor.authorSivrikaya, Osman
dc.contributor.authorSoycan, Taner Y.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2011
dc.departmentNiğde ÖHÜ
dc.description.abstractThe determination of the compaction parameters such as optimum water content (w(opt)) and maximum dry unit weight (gamma(dmax)) requires great efforts by applying the compaction testing procedure which is also time consuming and needs significant amount of work. Therefore, it seems more reasonable to use the indirect methods for estimating the compaction parameters. In recent years, the artificial neural network (ANN) modelling has gained an increasing interest and is also acquiring more popularity in geotechnical engineering applications. This study deals with the estimation of the compaction parameters for fine-grained soils based on compaction energy using ANN with the feed-forward back-propagation algorithm. In this study, the data including the results of the consistency tests, standard and modified Proctor tests, are collected from the literature and used in the analyses. The optimum structure of a network is determined for each ANN models. The analyses showed that the ANN models give quite reliable estimations in comparison with regression methods, thus they can be used as a reliable tool for the prediction of w(opt) and gamma(dmax). Copyright (C) 2010 John Wiley & Sons, Ltd.
dc.identifier.doi10.1002/nag.981
dc.identifier.endpage1841
dc.identifier.issn0363-9061
dc.identifier.issue17
dc.identifier.scopus2-s2.0-81155146014
dc.identifier.scopusqualityQ1
dc.identifier.startpage1830
dc.identifier.urihttps://dx.doi.org/10.1002/nag.981
dc.identifier.urihttps://hdl.handle.net/11480/4662
dc.identifier.volume35
dc.identifier.wosWOS:000297497300002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherWILEY-BLACKWELL
dc.relation.ispartofINTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectsoil compaction
dc.subjectfine-grained soils
dc.subjectindex properties
dc.subjectartificial neural network
dc.titleEstimation of compaction parameters of fine-grained soils in terms of compaction energy using artificial neural networks
dc.typeArticle

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