Kinematic-based failure angle analysis for discontinuity-controlled rock slope instabilities: a case study of Ren Yi Peak Cluster in Fusong County, China

Jianhua Yan, Jianping Chen (), Yuchao Li, Zhihai Li, Yansong Zhang, Xin Zhou, Qaiser Mehmood, Jing Liu and Zhou Wang
Additional contact information
Jianhua Yan: Jilin University
Jianping Chen: Jilin University
Yuchao Li: Jilin University
Zhihai Li: Jilin University
Yansong Zhang: Jilin University
Xin Zhou: Chongqing Jiaotong University
Qaiser Mehmood: Jilin University
Jing Liu: Jilin University
Zhou Wang: Jilin University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 3, No 6, 2296 pages

Abstract: Abstract Failure angle, also called maximum safe slope angle, is an important design parameter for rock slope stability analysis and slope excavation. This study aims to scientifically acquire the failure angle of the jointed rock slope, and a method incorporating the kinematic analysis, Bayesian estimation and Monte Carlo simulation is proposed. The probabilistic kinematic analysis could consider the variety of discontinuity properties. The Bayesian estimation and Monte Carlo simulation could consider the varieties and uncertainties of the failure angle data. The proposed method is applied to a discontinuity-controlled rock slope located in the Fusong County, Jilin Province, China. Numerous widely scattered failure angle data is firstly obtained using the probabilistic kinematic analysis. Then the Bayesian estimation and Monte Carlo simulation are applied to determine the failure angle scientifically. The suggested failure angle of west, middle and east section of the studied slope are 65.67°, 65.79° and 64.76°, respectively. The probabilities of kinematic instabilities are also obtained based on the failure angle data. In general, the studied slope is stable and suitable to develop projects.

Keywords: Kinematic analysis; Failure angle; Discontinuity; Rock slope; Bayesian statistic; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-05137-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:111:y:2022:i:3:d:10.1007_s11069-021-05137-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-05137-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().