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dc.contributor.authorEtemad-Shahidi, A
dc.contributor.authorYasa, R
dc.contributor.authorKazeminezhad, MH
dc.date.accessioned2017-05-03T13:12:34Z
dc.date.available2017-05-03T13:12:34Z
dc.date.issued2011
dc.date.modified2012-05-28T22:37:58Z
dc.identifier.issn0141-1187
dc.identifier.doi10.1016/j.apor.2010.11.002
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent261390 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom54
dc.relation.ispartofpageto59
dc.relation.ispartofissue1
dc.relation.ispartofjournalApplied Ocean Research
dc.relation.ispartofvolume33
dc.rights.retentionY
dc.subject.fieldofresearchOceanography
dc.subject.fieldofresearchOceanography not elsewhere classified
dc.subject.fieldofresearchCivil engineering
dc.subject.fieldofresearchResources engineering and extractive metallurgy
dc.subject.fieldofresearchcode3708
dc.subject.fieldofresearchcode370899
dc.subject.fieldofresearchcode4005
dc.subject.fieldofresearchcode4019
dc.titlePrediction of wave-induced scour depth under submarine pipelines using machine learning approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 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.
gro.date.issued2011
gro.hasfulltextFull Text
gro.griffith.authorEtemad Shahidi, Amir F.


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