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

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Chengxin Feng
  • Marcos A. Valdebenito
  • Marcin Chwała
  • Kang Liao
  • Matteo Broggi
  • Michael Beer

Research Organisations

External Research Organisations

  • TU Dortmund University
  • Wroclaw University of Technology
  • Southwest Jiaotong University
  • University of Liverpool
  • Tongji University
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Details

Original languageEnglish
Pages (from-to)1140-1152
Number of pages13
JournalJournal of Rock Mechanics and Geotechnical Engineering
Volume16
Issue number4
Early online date4 Nov 2023
Publication statusPublished - Apr 2024

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.

Keywords

    Latinized partial stratified sampling, Maximum entropy distribution, Random field, Reliability analysis, Slope

ASJC Scopus subject areas

Cite this

Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments. / Feng, Chengxin; Valdebenito, Marcos A.; Chwała, Marcin et al.
In: Journal of Rock Mechanics and Geotechnical Engineering, Vol. 16, No. 4, 04.2024, p. 1140-1152.

Research output: Contribution to journalArticleResearchpeer review

Feng, C, Valdebenito, MA, Chwała, M, Liao, K, Broggi, M & Beer, M 2024, 'Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments', Journal of Rock Mechanics and Geotechnical Engineering, vol. 16, no. 4, pp. 1140-1152. https://doi.org/10.1016/j.jrmge.2023.09.006
Feng, C., Valdebenito, M. A., Chwała, M., Liao, K., Broggi, M., & Beer, M. (2024). Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments. Journal of Rock Mechanics and Geotechnical Engineering, 16(4), 1140-1152. https://doi.org/10.1016/j.jrmge.2023.09.006
Feng C, Valdebenito MA, Chwała M, Liao K, Broggi M, Beer M. Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments. Journal of Rock Mechanics and Geotechnical Engineering. 2024 Apr;16(4):1140-1152. Epub 2023 Nov 4. doi: 10.1016/j.jrmge.2023.09.006
Feng, Chengxin ; Valdebenito, Marcos A. ; Chwała, Marcin et al. / Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments. In: Journal of Rock Mechanics and Geotechnical Engineering. 2024 ; Vol. 16, No. 4. pp. 1140-1152.
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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.",
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author = "Chengxin Feng and Valdebenito, {Marcos A.} and Marcin Chwa{\l}a and Kang Liao and Matteo Broggi and Michael Beer",
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Download

TY - JOUR

T1 - Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

AU - Feng, Chengxin

AU - Valdebenito, Marcos A.

AU - Chwała, Marcin

AU - Liao, Kang

AU - Broggi, Matteo

AU - Beer, Michael

N1 - Funding Information: We acknowledge the funding support from the China Scholarship Council (CSC).

PY - 2024/4

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N2 - 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.

AB - 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.

KW - Latinized partial stratified sampling

KW - Maximum entropy distribution

KW - Random field

KW - Reliability analysis

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