Prediction of wave-induced scour depth under submarine pipelines using machine learning approach

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Title Prediction of wave-induced scour depth under submarine pipelines using machine learning approach
Author Etemad Shahidi, Amir Farshad; Yasa, R.; Kazeminezhad, M.H.
Journal Name Applied Ocean Research
Year Published 2011
Place of publication United Kingdom
Publisher Elsevier
Abstract The 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.
Peer Reviewed Yes
Published Yes
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Copyright Statement 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.
Volume 33
Issue Number 1
Page from 54
Page to 59
ISSN 0141-1187
Date Accessioned 2012-03-28
Language en_US
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Oceanography
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1x

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