Probabilistic Analysis of Slopes with Linearly Increasing Undrained Shear Strength Using RLEM Approach

Javankhoshdel, Sina; Cami, Brigid; Chenari, Reza Jamshidi; Dastpak, Pooya

doi:10.1007/s40515-020-00118-7

Cited by 14 publications

(5 citation statements)

References 35 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%

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%

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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“…The slope has been studied in the literature (Cho, 2010;Jiang et al, 2015;Liu et al, 2017;Liu et al, 2018), and the results from these available studies can be easily referred to. A similar slope study case was performed in Javankhoshdel et al (2020). The cross-section of the slope is shown in Figure 6A, where the slope height and slope angle are 10 m and The COV values of c and φ are 0.3 and 0.2, respectively.…”

Section: Example Iii: a Cohesive-frictional Slope Considering 2-d Isvmentioning

confidence: 99%

“…Then, reliability approaches, for example, the Monte Carlo simulation (MCS) and the first-order reliability method (FORM), are utilized to calculate the probability of failure (P f ) or the reliability index (β) of a slope based on a conventional deterministic slope stability analysis model; for example, the limit equilibrium method (LEM) and the finite element method (FEM). Among the deterministic methods, LEM is the one most used with probabilistic approaches for slope reliability analysis because of its simplicity and wide applications in conventional slope designs (Javankhoshdel et al, 2020;Mafi et al, 2020). LEM, however, searches for the factor of safety (FS) of slopes among a large number of potential slip surfaces (PSSs), which might be time-consuming (Zhang et al, 2011).…”

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confidence: 99%

Efficient slope reliability analysis based on representative slip surfaces: a comparative study

Zhu

Zhang

et al. 2023

Front. Earth Sci.

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Slope reliability analysis can be conducted based on representative slip surfaces (RSSs) more efficiently than the conventional analysis based on many potential slip surfaces (PSSs). Various methods for selecting RSSs are proposed to enhance the efficiency of slope reliability analysis. These methods, however, generally require a complex calculation procedure (e.g., evaluation of reliability index for each PSS and/or correlation coefficients among PSSs) that cannot adaptively single out the RSSs, and the selected RSSs by these methods are commonly related to the statistics of soil properties. This leads to the question of how to efficiently and adaptively identify the RSSs of a slope for a subsequent reliability analysis with many parametric studies. To answer this question, an adaptive K-means clustering-based RSSs (AKCBR) selection method has been recently developed that is able to select the RSSs adaptively and efficiently from many PSSs. The RSSs identified by AKCBR do not vary with the variation of soil statistics, such as the inherent spatial variability that is beneficial to slope reliability analysis involving many parametric studies. As such, limitations of the available methods are tackled in AKCBR. A comprehensive comparative study is conducted in this paper to explore in detail the strength and weaknesses of the AKCBR against the available methods. Four slope examples that represent four kinds of slope stability problems are considered. Results show that AKCBR provides reliability results comparable with the available methods in terms of probability of failure and the most dominant failure modes, and it is generally more efficient. The AKCBR can adaptively identify the RSSs of slopes belonging to different types, and the RSSs are statistically robust against the statistics of soil properties, which is beneficial to reliability analysis involving many parametric studies.

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“…The slope stability analysis primarily focuses on determining a safety index called the factor of safety (FS) to identify the critical failure surface (CFS). The FS value of any slope against failure can be determined using either the limit equilibrium (LE) method [1,2,3,4], finite element method (FEM)-based strength reduction technique [5,6,7,8], limit analysis method [9,10,11,12,13], and random limit equilibrium methods (RLEM) [14,15]. The LE method defines FS of any slope as the ratio of the resisting forces/moment trying to stabilize the slope to the driving forces/moment responsible for destabilizing it.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Limit Equilibrium-Based Platform Incorporating Simplified Bishop, Janbu and Morgenstern–Price Methods for Soil Slope Stability Problems

Alok,

Burman,

Samui

et al. 2024

Advances in Civil Engineering

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Limit equilibrium (LE) method is the most widely used method for slope stability analysis. Different methods based on the LE technique for the analysis of the stability of the slope have been developed. Some are based on satisfying the force equilibrium condition of the failing mass (Janbu’s method), while some focus on satisfying the moment equilibrium condition (Bishop’s method). Among these methods, the most accurate result is provided by the Morgenstern–Price method as it not only satisfies both moments as well as a force equilibrium condition but also considers the interslice shear forces (Vi) and interslice normal forces (Ei), which are neglected by most of the LE methods to avoid the condition of indeterminacy. To accommodate these forces, Morgenstern–Price (MP) gave a relation between the Vi and Ei which depends upon a scaling multiplier (λ). Thus, it becomes necessary to evaluate λ value along with the factor of safety (FS). There is barely any work discussing the detailed methodology of evaluation of λ along with FS. Method for obtaining λ along with FS have been developed and elaborated in details here. While calculating FS (MP method), evaluation of Ei is a must which is dependent upon the values of normal force at the base of each slice (Ni) and FS, which itself is dependent upon the value of Ei, making it a loop of interdependent variables. To avoid this interdependency of above stated variables, a separate formulation of Ei is given which reduces the calculations (run-time) involved. A VBA code-based platform has also been developed incorporating the generalized LE method, including Bishop’s, Janbu’s, and Morgenstern–Price methods which are represented in the form of flowcharts in this work.

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Probabilistic Analysis of Slopes with Linearly Increasing Undrained Shear Strength Using RLEM Approach

Cited by 14 publications

References 35 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

Efficient slope reliability analysis based on representative slip surfaces: a comparative study

A Generalized Limit Equilibrium-Based Platform Incorporating Simplified Bishop, Janbu and Morgenstern–Price Methods for Soil Slope Stability Problems

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