Machine learning aided stochastic slope stability analysis

Liu, Z; Wu, D; Sheng, D; Fatahi, B; Khabbaz, H

Machine learning aided stochastic slope stability analysis

Liu, Z Wu, D

Sheng, D

Fatahi, B

Khabbaz, H

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Publisher:: Eccomas Proceedia
Publication Type:: Conference Proceeding
Citation:: UNCECOMP Proceedings, 2021, 2021-June, pp. 75-81
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Liu, Z
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.contributor.author	Fatahi, B https://orcid.org/0000-0002-7920-6946
dc.contributor.author	Khabbaz, H
dc.date	2021-06-28
dc.date.accessioned	2022-06-26T03:47:24Z
dc.date.available	2022-06-26T03:47:24Z
dc.date.issued	2021-01-01
dc.identifier.citation	UNCECOMP Proceedings, 2021, 2021-June, pp. 75-81
dc.identifier.issn	2623-3339
dc.identifier.issn	2623-3339
dc.identifier.uri	http://hdl.handle.net/10453/158369
dc.description.abstract	This paper presents the study in the machine learning aided stochastic slope stability analysis through the finite element method. The probability of failure of a dam with cohesive slope has been investigated. The numerical model has been built by the finite element method. An advanced machine learning algorithm called Extreme Learning Machine (ELM) is adopted to establish the regression model. The applicability and effectiveness of the presented approach are compared by the Monte-Carlo simulation method.
dc.language	en
dc.publisher	Eccomas Proceedia
dc.relation.ispartof	UNCECOMP Proceedings
dc.relation.ispartof	ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering
dc.relation.isbasedon	10.7712/120221.8023.19068
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine learning aided stochastic slope stability analysis
dc.type	Conference Proceeding
utslib.citation.volume	2021-June
utslib.location.activity	Streamed from Athens, Greece,
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/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-26T03:47:23Z
pubs.finish-date	2021-06-30
pubs.publication-status	Published
pubs.start-date	2021-06-28
pubs.volume	2021-June

Abstract:

This paper presents the study in the machine learning aided stochastic slope stability analysis through the finite element method. The probability of failure of a dam with cohesive slope has been investigated. The numerical model has been built by the finite element method. An advanced machine learning algorithm called Extreme Learning Machine (ELM) is adopted to establish the regression model. The applicability and effectiveness of the presented approach are compared by the Monte-Carlo simulation method.

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http://hdl.handle.net/10453/158369