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

Cha, D; Zhang, H; Blumenstein, M

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

Cha, D Zhang, H Blumenstein, M

Permalink

Publication Type:: Journal Article
Citation:: Ocean Engineering, 2011, 38 (7), pp. 878 - 887
Issue Date:: 2011-05-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0029801810001848-main.pdf	Published Version	1.2 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cha, D	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2011-05-01	en_US
dc.identifier.citation	Ocean Engineering, 2011, 38 (7), pp. 878 - 887	en_US
dc.identifier.issn	0029-8018	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115816
dc.description.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. © 2010 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Ocean Engineering	en_US
dc.relation.isbasedon	10.1016/j.oceaneng.2010.08.002	en_US
dc.subject.classification	Civil Engineering	en_US
dc.title	Prediction of maximum wave-induced liquefaction in porous seabed using multi-artificial neural network model	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	38	en_US
utslib.for	0911 Maritime Engineering	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0405 Oceanography	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

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. © 2010 Elsevier Ltd. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115816