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01.04.2022 | State-of-the-art paper

Probabilistic slope stability analysis: state-of-the-art review and future prospects

verfasst von: Rubi Chakraborty, Arindam Dey

Erschienen in: Innovative Infrastructure Solutions | Ausgabe 2/2022

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Abstract

Conventionally adopted deterministic slope stability analyses do not consider the influence of uncertainties related to geotechnical properties as well as failure mechanism in slope stability assessments. In this regard, probabilistic study of slope stability rationally incorporates the influence of parameter uncertainty. This paper presents a comprehensive review of the currently available probabilistic methods, their evolution and their recent advancements with respect to slope stability analyses. A description about the different approaches is provided, including the approximate approaches and Monte Carlo simulation-based approaches. The efficiencies and shortcoming of each of these methods and their evolution in dealing with probabilistic analyses of slopes are elucidated. The influence of the uncertainties related to soil heterogeneity inherent spatial variability of shear strength parameters and geological uncertainty on the probabilistic slope stability analyses are discussed. The critical review brings out that incorporation of geological uncertainty and probabilistic seismic slope stability analyses needs lot of research and development. It is also noted that probabilistic stability assessment of retained or reinforced natural slopes are yet to receive proper attention from the geotechnical engineering fraternity. This review article would aid the readers in a critical and comprehensive knowledge of the existing developments in probabilistic slope stability analyses, while highlighting the pathway for future research.

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Titel: Probabilistic slope stability analysis: state-of-the-art review and future prospects
verfasst von: Rubi Chakraborty
Arindam Dey
Publikationsdatum: 01.04.2022
Verlag: Springer International Publishing
Erschienen in: Innovative Infrastructure Solutions / Ausgabe 2/2022
Print ISSN: 2364-4176
Elektronische ISSN: 2364-4184
DOI: https://doi.org/10.1007/s41062-022-00784-1

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