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

verfasst von: 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.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): Technische Universität Dortmund
Wroclaw University of Technology
Southwest Jiaotong University
The University of Liverpool
Tongji University
Typ: Artikel
Journal: Journal of Rock Mechanics and Geotechnical Engineering
Band: 16
Seiten: 1140-1152
Anzahl der Seiten: 13
ISSN: 1674-7755
Publikationsdatum: 04.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geotechnik und Ingenieurgeologie
Elektronische Version(en): https://doi.org/10.1016/j.jrmge.2023.09.006 (Zugang: Offen)

BibTeX

@article{d1ef42c975cc4971a099e93282681a88,
title = "Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments",
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-Lo{\`e}ve 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.",
keywords = "Latinized partial stratified sampling, Maximum entropy distribution, Random field, Reliability analysis, Slope",
author = "Chengxin Feng and Valdebenito, {Marcos A.} and Marcin Chwa{\l}a and Kang Liao and Matteo Broggi and Michael Beer",
note = "Funding Information: We acknowledge the funding support from the China Scholarship Council (CSC). ",
year = "2024",
month = apr,
doi = "10.1016/j.jrmge.2023.09.006",
language = "English",
volume = "16",
pages = "1140--1152",
journal = "Journal of Rock Mechanics and Geotechnical Engineering",
issn = "1674-7755",
publisher = "Chinese Academy of Sciences",
number = "4",
}