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dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorZhang, Hongen_US
dc.contributor.authorJeng, Dong-Shengen_US
dc.contributor.editorAjith Abraham, Crina Grosan and Witold Pedryczen_US
dc.date.accessioned2017-05-03T14:30:40Z
dc.date.available2017-05-03T14:30:40Z
dc.date.issued2008en_US
dc.date.modified2011-05-30T06:53:47Z
dc.identifier.isbn9783540753957en_US
dc.identifier.doi10.1007/978-3-540-75396-4_12en_AU
dc.identifier.urihttp://hdl.handle.net/10072/23640
dc.description.abstractIn the past decade, computational intelligence (CI) techniques have been widely adopted in various fields such as business, science and engineering, as well as information technology. Specifically, hybrid techniques using artificial neural networks (ANNs) and genetic algorithms (GAs) are becoming an important alternative for solving problems in the field of engineering in comparison to traditional solutions, which ordinarily use complicated mathematical theories. The wave-induced seabed liquefaction problem is one of the most critical issues for analysing and designing marine structures such as caissons, oil platforms and harbours. In the past, various investigations into wave-induced seabed liquefaction have been carried out including numerical models, analytical solutions and some laboratory experiments. However, most previous numerical studies are based on solving complicated partial differential equations. In this study, the proposed neural-genetic model is applied to wave-induced liquefaction, which provides a better prediction of liquefaction potential. The neural-genetic simulation results illustrate the applicability of the hybrid technique for the accurate prediction of wave-induced liquefaction depth, which can also provide coastal engineers with alternative tools to analyse the stability of marine sediments.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent797652 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringer Berlinen_US
dc.publisher.placeHeidelbergen_US
dc.publisher.urihttp://www.springerlink.com/en_AU
dc.relation.ispartofbooktitleEngineering Evolutionary Intelligent Systemsen_US
dc.relation.ispartofchapter12en_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofpagefrom337en_US
dc.relation.ispartofpageto351en_US
dc.relation.ispartofedition2008en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280212en_US
dc.subject.fieldofresearchcode291205en_US
dc.titleA Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depthen_US
dc.typeBook chapteren_US
dc.type.descriptionB1 - Book Chapters (HERDC)en_US
dc.type.codeB - Book Chaptersen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2008
gro.hasfulltextFull Text


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