Show simple item record

dc.contributor.authorEternad Shahidi, A.en_US
dc.contributor.authorYasa, R.en_US
dc.contributor.authorKazeminezhad, M.en_US
dc.date.accessioned2017-04-24T10:19:03Z
dc.date.available2017-04-24T10:19:03Z
dc.date.issued2011en_US
dc.date.modified2012-05-28T22:37:58Z
dc.identifier.issn01411187en_US
dc.identifier.doi10.1016/j.apor.2010.11.002en_US
dc.identifier.urihttp://hdl.handle.net/10072/44228
dc.description.abstractThe scour around submarine pipelines may influence their stability; therefore scour prediction is a very important issue in submarine pipeline design. Several investigations have been conducted to develop a relationship between wave-induced scour depth under pipelines and the governing parameters. However, existing formulas do not always yield accurate results due to the complexity of the scour phenomenon. Recently, machine learning approaches such as Artificial Neural Networks (ANNs) have been used to increase the accuracy of the scour depth prediction. Nevertheless, they are not as transparent and easy to use as conventional formulas. In this study, the wave-induced scour was studied in both clear water and live bed conditions using the M5' model tree as a novel soft computing method. The M5' model is more transparent and can provide understandable formulas. To develop the models, several dimensionless parameter, such as gap to diameter ratio, Keulegan-Carpenter number and Shields number were used. The results show that the M5' models increase the accuracy of the scour prediction and that the Shields number is very important in the clear water condition. Overall, the results illustrate that the developed formulas could serve as a valuable tool for the prediction of wave-induced scour depth under both live bed and clear water conditions.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent261390 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.ispartofpagefrom54en_US
dc.relation.ispartofpageto59en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalApplied Ocean Researchen_US
dc.relation.ispartofvolume33en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchOceanography not elsewhere classifieden_US
dc.subject.fieldofresearchcode040599en_US
dc.titlePrediction of wave-induced scour depth under submarine pipelines using machine learning approachen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2011 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


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record