Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5761
Full metadata record
DC FieldValueLanguage
dc.contributor.authorErzin, Yusuf-
dc.contributor.authorEcemiş, Nurhan-
dc.date.accessioned2017-06-14T07:34:56Z-
dc.date.available2017-06-14T07:34:56Z-
dc.date.issued2014-
dc.identifier.citationErzin, Y., and Ecemiş, N. (2014). The use of neural networks for CPT-based liquefaction screening. Bulletin of Engineering Geology and the Environment, 74(1), 103-116. doi:10.1007/s10064-014-0606-8en_US
dc.identifier.issn1435-9529-
dc.identifier.urihttps://doi.org/10.1007/s10064-014-0606-8-
dc.identifier.urihttp://hdl.handle.net/11147/5761-
dc.description.abstractThis study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofBulletin of Engineering Geology and the Environmenten_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectCone penetration resistanceen_US
dc.subjectLiquefaction resistanceen_US
dc.subjectOttowa sanden_US
dc.titleThe Use of Neural Networks for Cpt-Based Liquefaction Screeningen_US
dc.typeArticleen_US
dc.authoridTR115346en_US
dc.institutionauthorEcemiş, Nurhan-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume74en_US
dc.identifier.issue1en_US
dc.identifier.startpage103en_US
dc.identifier.endpage116en_US
dc.identifier.wosWOS:000348300600008en_US
dc.identifier.scopus2-s2.0-84922000533en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s10064-014-0606-8-
dc.relation.doi10.1007/s10064-014-0606-8en_US
dc.coverage.doi10.1007/s10064-014-0606-8en_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.fulltextWith Fulltext-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
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
Files in This Item:
File Description SizeFormat 
5761.pdfMakale452.14 kBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

35
checked on Dec 6, 2024

WEB OF SCIENCETM
Citations

33
checked on Oct 26, 2024

Page view(s)

350
checked on Dec 9, 2024

Download(s)

548
checked on Dec 9, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.