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dc.contributor.authorZhang, Hongen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.date.accessioned2017-04-24T08:44:00Z
dc.date.available2017-04-24T08:44:00Z
dc.date.issued2011en_US
dc.date.modified2013-05-29T08:46:03Z
dc.identifier.issn0029-8018en_US
dc.identifier.doi10.1016/j.oceaneng.2010.08.002en_US
dc.identifier.urihttp://hdl.handle.net/10072/37529
dc.description.abstractIn the last few decades, considerable efforts have been devoted to the phenomenon of wave-induced liquefactions, because it is one of the most important factors for analysing the seabed and designing marine structures. Although numerous studies of wave-induced liquefaction have been carried out, comparatively little is known about the impact of liquefaction on marine structures. Furthermore, most previous researches have focused on complicated mathematical theories and some laboratory work. In the present study, a data dependent approach for the prediction of the wave-induced liquefaction depth in a porous seabed is proposed, based on a multi-artificial neural network (MANN) method. Numerical results indicate that the MANN model can provide an accurate prediction of the wave-induced maximum liquefaction depth with 10% of the original database. This study demonstrates the capacity of the proposed MANN model and provides coastal engineers with another effective tool to analyse the stability of the marine sediment.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent551359 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom878en_US
dc.relation.ispartofpageto887en_US
dc.relation.ispartofissue7en_US
dc.relation.ispartofjournalOcean Engineeringen_US
dc.relation.ispartofvolume38en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchMaritime Engineering not elsewhere classifieden_US
dc.subject.fieldofresearchcode091199en_US
dc.titlePrediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network modelen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2010 Elsevier Inc. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_US
gro.date.issued2011
gro.hasfulltextFull Text


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