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Lookup NU author(s): Dr David MilledgeORCiD, Professor Stefano Utili
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023, The Author(s).We present a new computationally efficient methodology to estimate the probability of rainfall-induced slope failure based on mechanical probabilistic slope stability analyses coupled with a hydrogeological model of the upslope area. The model accounts for: (1) uncertainty of geotechnical and hydrogeological parameters; (2) rainfall precipitation recorded over a period of time; and (3) the effect of upslope topography. The methodology provides two key outputs: (1) time-varying conditional probability of slope failure; and (2) an estimate of the absolute frequency of slope failure over any time period of interest. The methodology consists of the following steps: first, characterising the uncertainty of the slope geomaterial strength parameters; second, performing limit equilibrium method stability analyses for the realisations of the geomaterial strength parameters required to calculate the slope probability of failure by a Monte Carlo Simulation. The stability analyses are performed for various phreatic surface heights. These phreatic surfaces are then matched to a phreatic surface time series obtained from the 1D Hillslope-Storage Boussinesq model run for the upslope area to generate Factor of Safety (FoS) time series. A time-varying conditional probability of failure and an absolute frequency of slope failure can then be estimated from these FoS time series. We demonstrate this methodology on a road slope cutting in Nepal where geotechnical tests are not readily conducted. We believe this methodology improves the reliability of slope safety estimates where site investigation is not possible. Also, the methodology enables practitioners to avoid making unrealistic assumptions on the hydrological input. Finally, we find that the time-varying failure probability shows marked variations over time as a result of the monsoon wet–dry weather.
Author(s): Robson E, Milledge D, Utili S, Dattola G
Publication type: Article
Publication status: Published
Journal: Rock Mechanics and Rock Engineering
Year: 2024
Pages: epub ahead of print
Online publication date: 24/12/2023
Acceptance date: 20/11/2023
Date deposited: 12/02/2024
ISSN (print): 0723-2632
ISSN (electronic): 1434-453X
Publisher: Springer
URL: https://doi.org/10.1007/s00603-023-03694-5
DOI: 10.1007/s00603-023-03694-5
Data Access Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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