Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6761
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dc.contributor.authorErzin, Yusuf-
dc.contributor.authorEcemiş, Nurhan-
dc.date.accessioned2018-01-26T13:35:25Z-
dc.date.available2018-01-26T13:35:25Z-
dc.date.issued2017-12-
dc.identifier.citationErzin, Y., and Ecemiş, N. (2017). The use of neural networks for the prediction of cone penetration resistance of silty sands. Neural Computing and Applications, 28, 727-736. doi:10.1007/s00521-016-2371-zen_US
dc.identifier.issn0941-0643-
dc.identifier.issn1433-3058-
dc.identifier.urihttp://doi.org/10.1007/s00521-016-2371-z-
dc.identifier.urihttp://hdl.handle.net/11147/6761-
dc.description.abstractIn this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.en_US
dc.description.sponsorshipEuropean Union (IRG248218); TUBITAK Project (111M602)en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCone penetration resistanceen_US
dc.subjectArtificial neural networksen_US
dc.subjectSilty sanden_US
dc.subjectHorizontal coefficient of consolidationen_US
dc.titleThe use of neural networks for the prediction of cone penetration resistance of silty sandsen_US
dc.typeArticleen_US
dc.authoridTR115346en_US
dc.institutionauthorEcemiş, Nurhan-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume28en_US
dc.identifier.startpage727en_US
dc.identifier.endpage736en_US
dc.identifier.wosWOS:000417319700060en_US
dc.identifier.scopus2-s2.0-84976271906en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s00521-016-2371-z-
dc.relation.doi10.1007/s00521-016-2371-zen_US
dc.coverage.doi10.1007/s00521-016-2371-zen_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.openairetypeArticle-
crisitem.author.dept03.03. Department of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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