Direct search for minimum reliability index of earth slopes

Bhattacharya, Gautam; Jana, Dipak Kumar; Ojha, Sarita; Chakraborty, Subrata

doi:10.1016/s0266-352x(03)00059-4

Cited by 100 publications

(47 citation statements)

References 18 publications

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“…El-Ramly (2002) hence noted that an essential part of the probabilistic analysis is to consider all the possibly hazardous slip surfaces which include the deterministic critical slip surface, the minimum reliability index slip surface (or the maximum PF slip surface) and surface through weak layers. Bhattacharya et al (2003) proposed a numerical procedure for locating the slip surface of minimum reliability index for slope, but the algorithm has not been implemented in S lope/W (GEO-SLOPE 2004).…”

Section: Critical Situations In Slope Stability Analysesmentioning

confidence: 99%

Soil slope stability investigation and analysis in Iowa

Yang

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Section: Critical Situations In Slope Stability Analysesmentioning

confidence: 99%

Soil slope stability investigation and analysis in Iowa

Yang

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“…However, as the slip surface in question is still the deterministic critical slip surface, this approach is unlikely to find the true minimum reliability index. Over recent years researchers (Hassan & Wolff, 1999;Bhattacharya et al, 2003;Xue & Gavin, 2007, among others) have incorporated sophisticated techniques to search simultaneously for the critical probabilistic surface and critical design points of input parameters, allowing for a more accurate minimum reliability index. These approaches represent a significant advance on earlier approaches, but they still only provide the user with the most critical slip surface (lowest reliability index) for a given slope condition or state.…”

Section: Introductionmentioning

confidence: 99%

System reliability of slopes using multimodal optimisation

2016

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Many engineered and natural slopes have complex geometries and are multi-layered. For these slopes traditional stability analyses will tend to predict critical failure surfaces in layers with the lowest mean strength. A move toward probabilistic analyses allows a designer to account for uncertainties with respect to input parameters that allow for a more complete understanding of risk. Railway slopes, which in some cases were built more than 150 years ago, form important assets on the European rail network. Many of these structures were built at slope angles significantly higher than those allowed in modern design codes. Depending on the local geotechnical conditions these slopes may be susceptible to deepseated failure; however, a significant number of failures each year occur as shallow translational slips that develop during periods of high rainfall. Thus, for a given slope, two potential failure mechanisms might exist with very similar probabilities of failure. In this paper a novel multimodal optimisation algorithm ('Slips') that is capable of detecting all feasible probabilistic slip surfaces simultaneously is presented. The system reliability analysis is applied using polar co-ordinates, as this approach has been shown to be less sensitive to local numerical instabilities, which can develop due to discontinuities on the limit state surface. The approach is applied to two example slopes where the complexity in terms of stratification and slope geometry is varied. In addition the methodology is validated using a real-life case study involving failure of a complex slope.

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“…More recently, Zhang et al (2013) have found that for slopes with complex geometry, the Hassan and Wolff method is less accurate in locating the most critical slip surface. Bhattacharya et al (2003) proposed a procedure which is similar to the procedure for the determination of the deterministic critical slip surface. Subsequently quite a few research work were reported based on the Hassan and Wolff approach (El-Ramly et al 2002, 2003aZhang et al 2013) and the Bhattacharya et al approach (Khajehzadeh et al 2010;Liang and Xue-song 2012;Metya and Bhattacharya 2014).…”

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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Direct search for minimum reliability index of earth slopes

Cited by 100 publications

References 18 publications

Soil slope stability investigation and analysis in Iowa

Soil slope stability investigation and analysis in Iowa

System reliability of slopes using multimodal optimisation

Reliability Analysis of Earth Slopes Considering Spatial Variability

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