Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

authored by: Chengxin Feng, Marcos A. Valdebenito, Marcin Chwała, Kang Liao, Matteo Broggi, Michael Beer
Abstract: Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering. The latter is particularly true for slope stability assessment, where the effects of uncertainty are synthesized in the so-called probability of failure. This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint. In view of this issue, this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments. The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion. Then, failure probabilities are estimated employing maximum entropy distribution with fractional moments. The application of the proposed approach is examined with two examples: a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope. The results show that the proposed approach has excellent accuracy and high efficiency, and it can be applied straightforwardly to similar geotechnical engineering problems.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): TU Dortmund University
Wroclaw University of Technology
Southwest Jiaotong University
University of Liverpool
Tongji University
Type: Article
Journal: Journal of Rock Mechanics and Geotechnical Engineering
Volume: 16
Pages: 1140-1152
No. of pages: 13
ISSN: 1674-7755
Publication date: 04.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Geotechnical Engineering and Engineering Geology
Electronic version(s): https://doi.org/10.1016/j.jrmge.2023.09.006 (Access: Open)