LASSO algorithm and its application in slope stability analysis

Xie, ML; Ye, XY; Zhang, S; Sheng, DC

LASSO algorithm and its application in slope stability analysis

Xie, ML Ye, XY Zhang, S Sheng, DC

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Publication Type:: Journal Article
Citation:: Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, 2021, 43, (9), pp. 1724-1729
Issue Date:: 2021-09-01

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Field	Value	Language
dc.contributor.author	Xie, ML
dc.contributor.author	Ye, XY
dc.contributor.author	Zhang, S
dc.contributor.author	Sheng, DC
dc.date.accessioned	2022-05-09T05:29:48Z
dc.date.available	2022-05-09T05:29:48Z
dc.date.issued	2021-09-01
dc.identifier.citation	Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, 2021, 43, (9), pp. 1724-1729
dc.identifier.issn	1000-4548
dc.identifier.uri	http://hdl.handle.net/10453/157155
dc.description.abstract	By introducing the LASSO algorithm into the slope reliability analysis, an algorithm model to predict the safety factor of a slope is established, and the function of searching the dangerous area of the slope is realized. First, using the finite element software is used to implement the Monte Carlo algorithm and to obtain the data of slope reliability analysis. Second, the acquired data is analyzed by the LASSO algorithm. A model is established to predict the safety factor of the slope under the specific intensity parameter distribution. The predicted results are compared with those by the ordinary linear regression algorithm to confirm the superiority of the LASSO algorithm, and its application suggestions in long-term slope risk monitoring are put forward. Third, the LASSO algorithm is combined with the Monte Carlo simulation to search for the most dangerous areas that affect the stability of the slope under multiple simulation results. The results indicate that compared with the ordinary linear regression algorithm, the model established by the LASSO algorithm can accurately find out the most dangerous area that affects the stability of the slope. Therefore, the LASSO algorithm can provide new ideas for the long-term slope risk monitoring and slope reinforcement.
dc.language	en
dc.relation.ispartof	Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering
dc.relation.isbasedon	10.11779/CJGE202109018
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	Civil Engineering
dc.title	LASSO algorithm and its application in slope stability analysis
dc.type	Journal Article
utslib.citation.volume	43
utslib.for	0905 Civil Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-09T05:29:41Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	43
utslib.citation.issue	9

Abstract:

By introducing the LASSO algorithm into the slope reliability analysis, an algorithm model to predict the safety factor of a slope is established, and the function of searching the dangerous area of the slope is realized. First, using the finite element software is used to implement the Monte Carlo algorithm and to obtain the data of slope reliability analysis. Second, the acquired data is analyzed by the LASSO algorithm. A model is established to predict the safety factor of the slope under the specific intensity parameter distribution. The predicted results are compared with those by the ordinary linear regression algorithm to confirm the superiority of the LASSO algorithm, and its application suggestions in long-term slope risk monitoring are put forward. Third, the LASSO algorithm is combined with the Monte Carlo simulation to search for the most dangerous areas that affect the stability of the slope under multiple simulation results. The results indicate that compared with the ordinary linear regression algorithm, the model established by the LASSO algorithm can accurately find out the most dangerous area that affects the stability of the slope. Therefore, the LASSO algorithm can provide new ideas for the long-term slope risk monitoring and slope reinforcement.

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http://hdl.handle.net/10453/157155