Machine learning aided stochastic reliability analysis of spatially variable slopes

He, X; Xu, H; Sabetamal, H; Sheng, D

Machine learning aided stochastic reliability analysis of spatially variable slopes

He, X

Xu, H Sabetamal, H

Sheng, D

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Computers and Geotechnics, 2020, 126
Issue Date:: 2020-10-01

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Field	Value	Language
dc.contributor.author	He, X https://orcid.org/0000-0001-5336-3663
dc.contributor.author	Xu, H
dc.contributor.author	Sabetamal, H https://orcid.org/0000-0002-9237-2316
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2021-02-27T22:46:08Z
dc.date.available	2021-02-27T22:46:08Z
dc.date.issued	2020-10-01
dc.identifier.citation	Computers and Geotechnics, 2020, 126
dc.identifier.issn	0266-352X
dc.identifier.issn	1873-7633
dc.identifier.uri	http://hdl.handle.net/10453/146512
dc.description.abstract	© 2020 This paper presents machine learning aided stochastic reliability analysis of spatially variable slopes, which significantly reduces the computational efforts and gives a complete statistical description of the factor of safety with promising accuracy compared with traditional methods. Within this framework, a small number of traditional random finite-element simulations are conducted. The samples of the random fields and the calculated factor of safety are, respectively, treated as training input and output data, and are fed into machine learning algorithms to find mathematical models to replace finite-element simulations. Two powerful machine learning algorithms used are the neural networks and the support-vector regression with their associated learning strategies. Several slopes are examined including stratified slopes with 3 or 4 layers described by 4 or 6 random fields. It is found that with 200 to 300 finite-element simulations (finished in about 5 ~ 8 h), the machine-learning generated model can predict the factor of safety accurately, and a stochastic analysis of 105 samples takes several minutes. However, the same traditional analysis would require hundreds of days of computation.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Computers and Geotechnics
dc.relation.isbasedon	10.1016/j.compgeo.2020.103711
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0915 Interdisciplinary Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Machine learning aided stochastic reliability analysis of spatially variable slopes
dc.type	Journal Article
utslib.citation.volume	126
utslib.for	0905 Civil Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	0915 Interdisciplinary Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-27T22:46:00Z
pubs.publication-status	Published
pubs.volume	126

Abstract:

© 2020 This paper presents machine learning aided stochastic reliability analysis of spatially variable slopes, which significantly reduces the computational efforts and gives a complete statistical description of the factor of safety with promising accuracy compared with traditional methods. Within this framework, a small number of traditional random finite-element simulations are conducted. The samples of the random fields and the calculated factor of safety are, respectively, treated as training input and output data, and are fed into machine learning algorithms to find mathematical models to replace finite-element simulations. Two powerful machine learning algorithms used are the neural networks and the support-vector regression with their associated learning strategies. Several slopes are examined including stratified slopes with 3 or 4 layers described by 4 or 6 random fields. It is found that with 200 to 300 finite-element simulations (finished in about 5 ~ 8 h), the machine-learning generated model can predict the factor of safety accurately, and a stochastic analysis of 105 samples takes several minutes. However, the same traditional analysis would require hundreds of days of computation.

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http://hdl.handle.net/10453/146512