DOI: 10.1007/s11069-020-04403-z
论文题名: The global sensitivity analysis of slope stability based on the least angle regression
作者: Xu Z. ; Zhou X. ; Qian Q.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 105, 期: 3 起始页码: 2361
结束页码: 2379
语种: 英语
中文关键词: Computational efficiency
; Global sensitivity analysis (GSA)
; Least angle regression (LARS) algorithm
; Slope stability
英文摘要: The least angle sensitivity (LARS) algorithm is used to realize the global sensitivity analysis of slope stability, while simultaneously considering the effects of several geotechnical parameters on slope stability. In addition, the Sobol sequence is applied in the sample simulation to generate the geotechnical parameters, thereby increasing the accuracy of the results. Two cases are considered to investigate the effects of the geotechnical parameters on slope stability, and the accuracy and efficiency of the LARS algorithm are examined. The importance measure indexes obtained using the LARS algorithm are in good agreement with those obtained using the Monte Carlo (MC) method. To determine the importance measure indexes, the performance functions of the slope stability analysis are required to be run N times when using the LARS algorithm, which is 1 / (n· N+ 1) the required number for the MC method, where n and N represent the number of random variables and sample size, respectively. In this scenario, the computational efficiency is considerably increased. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169483
Appears in Collections: 气候变化与战略
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作者单位: School of Civil Engineering, Chongqing University, Chongqing City, China; State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, 174 Shazhengjie Rd., Chongqing, 400045, China; Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, 174 Shazhengjie Rd., Chongqing, 400045, China
Recommended Citation:
Xu Z.,Zhou X.,Qian Q.. The global sensitivity analysis of slope stability based on the least angle regression[J]. Natural Hazards,2021-01-01,105(3)