Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

Xu, H; He, X; Shan, F; Niu, G; Sheng, D

Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

Xu, H

He, X

Shan, F

Niu, G Sheng, D

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Modelling, 2023, 4, (4), pp. 426-453
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Xu, H https://orcid.org/0000-0002-6556-8831
dc.contributor.author	He, X https://orcid.org/0000-0001-5336-3663
dc.contributor.author	Shan, F https://orcid.org/0000-0001-7497-6768
dc.contributor.author	Niu, G
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2024-03-26T04:54:06Z
dc.date.available	2024-03-26T04:54:06Z
dc.date.issued	2023-12-01
dc.identifier.citation	Modelling, 2023, 4, (4), pp. 426-453
dc.identifier.issn	2673-3951
dc.identifier.issn	2673-3951
dc.identifier.uri	http://hdl.handle.net/10453/177186
dc.description.abstract	In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Modelling
dc.relation.isbasedon	10.3390/modelling4040025
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review
dc.type	Journal Article
utslib.citation.volume	4
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 - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-26T04:54:05Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	4

Abstract:

In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given.

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http://hdl.handle.net/10453/177186