Risk de-aggregation and system reliability analysis of slope stability using representative slip surfaces

Li, Liang; Wang, Yu; Cao, Zi-Jun; Chu, Xuesong

doi:10.1016/j.compgeo.2013.05.004

Cited by 99 publications

(65 citation statements)

References 32 publications

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“…Equation (4) works well for a slope with a single failure mode (Huang et al 2013). However, for a slope problem, there might exist multiple failure modes due to the spatial variability or stratification of geotechnical materials (e.g., Chowdhury and Xu 1995;Huang et al 2010;Wang et al 2011;Zhang et al 2011;Li et al 2013;Jiang et al 2015). To account for the contributions of different failure modes to the overall risk of slope failure, Huang et al (2013) extends Eq.…”

Section: Introductionmentioning

confidence: 97%

“…In the past few decades, several probabilistic analysis approaches have been developed to evaluate the P f or β of slope stability in geotechnical reliability community, such as the first order second moment method (e.g., Christian et al 1994;Hassan and Wolff 1999), first order reliability method (e.g., Low et al 1998;Cho 2013), and direct Monte Carlo Simulation (MCS) method (e.g., El-Ramly et al 2002;Li et al 2011;Zhang et al 2011;Li et al 2013Li et al , 2014Tang et al 2015) and its advanced variants (e.g., Ching et al 2009;Wang et al 2011). Although these efforts significantly facilitate the understanding and application of probability-based approaches in slope engineering, practicing engineers are reluctant to adopt them in slope engineering practice (e.g., El-Ramly et al 2002).…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation

et al. 2015

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Random finite element method (RFEM) provides a rigorous tool to incorporate spatial variability of soil properties into reliability analysis and risk assessment of slope stability. However, it suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels (e.g., slope failure probability P f <0.001). To address this problem, this study integrates RFEM with an advanced Monte Carlo Simulation (MCS) method called "Subset Simulation (SS)" to develop an efficient RFEM (i.e., SS-based RFEM) for reliability analysis and risk assessment of soil slopes. The proposed SS-based RFEM expresses the overall risk of slope failure as a weighed aggregation of slope failure risk at different probability levels and quantifies the relative contributions of slope failure risk at different probability levels to the overall risk of slope failure. Equations are derived for integrating SS with RFEM to evaluate the probability (P f ) and risk (R) of slope failure. These equations are illustrated using a soil slope example. It is shown that the P f and R are evaluated properly using the proposed approach. Compared with the original RFEM with direct MCS, the SS-based RFEM improves, significantly, the computational efficiency of evaluating P f and R. This enhances the applications of RFEM in the reliability analysis and risk assessment of slope stability. With the aid of improved computational efficiency, a sensitivity study is also performed to explore effects of vertical spatial variability of soil properties on R. It is found that the vertical spatial variability affects the slope failure risk significantly.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation

et al. 2015

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“…An in-house Fortran-based software package for deterministic slope stability analysis has been developed and used successfully in previous studies (Li et al, 2010;Chu, 2011, 2014;Li et al, 2013aLi et al, , 2013b. The software package is equipped with HS algorithm and particle swarm optimization algorithm for efficient location of the deterministic critical slip surface.…”

Section: Software Package For Deterministic Stability Analysismentioning

confidence: 99%

Comparative study on response surfaces for reliability analysis of spatially variable soil slope

Chu

2015

China Ocean Eng

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This paper focuses on the performance of the second-order polynomial-based response surfaces on the reliability of spatially variable soil slope. A single response surface constructed to approximate the slope system failure performance function G(X) (called single RS) and multiple response surfaces constructed on finite number of slip surfaces (called multiple RS) are developed, respectively. Single RS and multiple RS are applied to evaluate the system failure probability p f for a cohesive soil slope together with Monte Carlo simulation (MCS). It is found that p f calculated by single RS deviates significantly from that obtained by searching a large number of potential slip surfaces, and this deviation becomes insignificant with the decrease of the number of random variables or the increase of the scale of fluctuation. In other words, single RS cannot approximate G(X) with reasonable accuracy. The value of p f from multiple response surfaces fits well with that obtained by searching a large number of potential slip surfaces. That is, multiple RS can estimate G(X) with reasonable accuracy.

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“…El-Ramly et al (2002) modeled the spatial variability of each input variable along the slip surface by a 1D stationary random field describing an elaborate spatial variability discretization model. A few others (e.g., Hong and Roh 2008;Wang et al 2011;Li et al 2013) also modeled the spatial variability of soil properties by a 1D random field; but they considered spatial variation along the vertical direction. It was, however, argued that if only the vertical autocorrelation distance is considered, it might result in some of the variables having no effect on the critical slip surface .…”

Section: Introductionmentioning

confidence: 99%

“…Those based on the LEM include Li and Lumb (1987), ElRamly et al (2002), Low (2003), Babu and Mukesh (2004), Cho (2007), Hong and Roh (2008), Wang et al (2011), Ji et al (2012) and Li et al (2013). El-Ramly et al (2002) modeled the spatial variability of each input variable along the slip surface by a 1D stationary random field describing an elaborate spatial variability discretization model.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Earth Slopes Considering Spatial Variability

Metya

Bhattacharya

2015

Geotech Geol Eng

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The paper presents a computational procedure for reliability analysis of earth slopes considering spatial variability of soils under the framework of the Limit Equilibrium Method. In the reliability analysis of earth slopes, the effect of spatial variability of soil properties is generally included indirectly by assuming that the probabilistic critical slip surface is the same as that determined without considering spatial variability. In contrast to this indirect approach, in the direct approach, the effect of spatial variability is included in the process of determination of the probabilistic critical surface itself. While the indirect approach requires much less computational effort, the direct approach is definitely more rigorous. In this context this paper attempts to investigate, with the help of numerical examples, how far away are the results obtained from the indirect approach from that obtained from the direct approach. In both the approaches, it is required to use a model of discretization of random fields into finite random variables. A few such models are available in the literature for onedimensional (1D) as well as two-dimensional (2D) spatial variability. The developed computational scheme is based on the First Order Reliability Method (FORM) coupled with the Spencer Method of Slices valid for limit equilibrium analysis of general slip surfaces. The study includes bringing out the computational advantages and disadvantages of the three commonly used discretization models. The sensitivity of the reliability index to the magnitudes of the scales of fluctuation has also been studied.

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Risk de-aggregation and system reliability analysis of slope stability using representative slip surfaces

Cited by 99 publications

References 32 publications

Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation

Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation

Comparative study on response surfaces for reliability analysis of spatially variable soil slope

Reliability Analysis of Earth Slopes Considering Spatial Variability

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