A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth

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Title A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth
Author Cha, Deaho Fred; Blumenstein, Michael Myer; Zhang, Hong; Jeng, Dong-Sheng
Book Title Engineering Evolutionary Intelligent Systems
Editor Ajith Abraham, Crina Grosan and Witold Pedrycz
Year Published 2008
Place of publication Heidelberg
Publisher Springer Berlin
Abstract In 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.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.springerlink.com/
Alternative URI http://dx.doi.org/10.1007/978-3-540-75396-4_12
Volume 82/2008
Edition 2008
Chapter Number 12
Page from 337
Page to 351
ISBN 9783540753957
Date Accessioned 2009-02-12
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Neural Networks, Genetic Alogrithms and Fuzzy Logic; PRE2009-Ocean Engineering
URI http://hdl.handle.net/10072/23640
Publication Type Book Chapters
Publication Type Code b1

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