Discussion of “An analytical probabilistic analysis of slopes based on limit equilibrium methods” by A. Johari, S. Mousavi. November 2018, DOI: https://doi.org/10.1007/s10064-018-1408-1

Chenari, Reza Jamshidi; Izadi, Ardavan

doi:10.1007/s10064-019-01473-7

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Several researchers in literature have investigated the impact of the spatial variability of soil strength and stiffness parameters on the stability of geo-structures (Javankhoshdel et al 2022). The importance of spatial variability of soil properties has been investigated through several studies of slope stability analysis (Javankhoshdel and Bathurst 2014;Luo et al 2016;Bathurst 2016, 2017;Jamshidi Chenari and Izadi 2019;Jamshidi Chenari et al 2020;Shah Malekpour et al 2020;Javankhoshdel et al 2020;Mafi et al 2020).…”

Section: Pf =mentioning

confidence: 99%

“…The Random Limit Equilibrium Method (RLEM) has recently been implemented commercially in the Slide2 Rocscience software (Rocscience 2022) which allows for stochastic slope stability analyses. Bathurst (2014, 2016), Izadi (2019) are examples of studies considering only circular slip surface assumptions in their RLEM analyses. Lately, with the introduction of optimization techniques in geotechnical field such as Cuckoo search and Particle Swarm Optimization (PSO), which are global optimization techniques together with the Surface Altering Optimization (SAO) which is a local optimization technique (as introduced by Mafi et al 2020), Tabarroki et al (2013), and Cami et al (2018) put into practice the implementation of non-circular failure surface in their slope stability analyses.…”

Section: Pf =mentioning

confidence: 99%

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A New Stochastic Response Surface Method in Spatial Variability Slope Stability Analysis

Javankhoshdel¹,

Zeger²,

Cami³

et al. 2023

Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)

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Spatial variability is becoming more and more common in limit equilibrium slope stability analysis. With the recent advances in the abilities for limit equilibrium slope stability analysis to handle complicated models, it is more important than ever to have a fast and accurate method to perform slope stability analysis in spatially varying soils. The use of response surfaces is widely adopted in the literature to deliver efficient stochastic analyses. However, their implementation is usually deeply correlated with the choice of a field generation algorithm. This makes the response surface method inflexible since it sometimes requires the field generation algorithm to be completely rewritten. Finely discretized spatially varying random fields can have many random variables which make the response surface difficult to determine. To solve this, the authors have proposed a modified response surface guided approach to slope reliability analysis in spatially varying soils which is independent of the choice of field generation algorithm. This approach was evaluated using a two-dimensional problem, and validated by comparing its accuracy and speed against the traditional Monte Carlo simulation.

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Section: Pf =mentioning

confidence: 99%

Section: Pf =mentioning

confidence: 99%

A New Stochastic Response Surface Method in Spatial Variability Slope Stability Analysis

Javankhoshdel¹,

Zeger²,

Cami³

et al. 2023

Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)

View full text Add to dashboard Cite

show abstract

“…An important source of uncertainty in the analysis of slope stability is the inherent spatial variability of soil parameters. The impacts of soil spatial variability on slope reliability analysis have been thoroughly examined using random field theory throughout the past few years (Griffiths and Fenton 2004;Griffiths et al 2009;Hicks and Spencer 2010;Li et al 2015;Jiang et al 2014;Hicks et al 2014;Liu et al 2017;Jamshidi Chenari and Alaie 2015;Javankhoshdel et al 2017;Liu et al 2018;Cami et al 2018;Jamshidi Chenari and Izadi 2019;Shah Malekpoor et al 2020;Javankhoshdel et al 2020;Mafi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Analysis of a Slope Using RLEM and Cross-Correlated Conditional Random Field

Javankhoshdel¹,

Mohammadi²,

Chenari³

et al. 2023

Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)

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Probabilistic analyses of slopes using Random Limit Equilibrium Method (RLEM) have been extensively reported in literature. However, in these types of analyses, the generated random fields are based on assumed values of horizontal and vertical correlation lengths. In practice, horizontal and vertical correlation lengths can be measured using CPT data and the data can be used to condition the generated random fields. Conditioning random fields reduces the level of uncertainty in the analysis and helps the simulations to render more reasonable results. In this study, the stability analysis of a simple slope is used to investigate the influence of conditional and unconditional random fields. To generate spatially variable fields, first, some artificial borehole data are employed to correlate the spatially variable friction angle field. Then, considering some typical values for the variability of the cohesion random field and the possible cross-correlation between the two fields, a couple of scenarios are defined to synthesize the spatially variable realizations of the cohesion field. Then, the results of cross-correlated conditioned and unconditioned random fields are compared. The results show that conditioning random field and considering the cross-correlation between soil input parameters significantly reduce the probability of slope failure.

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Stability analysis of anhydrite mine-out as an underground gas storage based on DEM and similarity theory: a case study

Wang

Zhang

et al. 2022

Bull Eng Geol Environ

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Discussion of “An analytical probabilistic analysis of slopes based on limit equilibrium methods” by A. Johari, S. Mousavi. November 2018, DOI: https://doi.org/10.1007/s10064-018-1408-1

Cited by 7 publications

References 7 publications

A New Stochastic Response Surface Method in Spatial Variability Slope Stability Analysis

A New Stochastic Response Surface Method in Spatial Variability Slope Stability Analysis

Probabilistic Analysis of a Slope Using RLEM and Cross-Correlated Conditional Random Field

Stability analysis of anhydrite mine-out as an underground gas storage based on DEM and similarity theory: a case study

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