Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model

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Title Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model
Author Cha, Deaho Fred; Zhang, Hong; Blumenstein, Michael Myer
Journal Name Ocean Engineering
Year Published 2011
Place of publication United Kingdom
Publisher Pergamon
Abstract In 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.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.oceaneng.2010.08.002
Copyright Statement Copyright 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.
Volume 38
Issue Number 7
Page from 878
Page to 887
ISSN 0029-8018
Date Accessioned 2011-01-05
Date Available 2013-05-29T08:46:03Z
Language en_US
Research Centre Centre for Infrastructure Engineering and Management; Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Maritime Engineering
URI http://hdl.handle.net/10072/37529
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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