System Reliability of Slopes by RFEM

Huang, Jinsong; Griffiths, D. V.; Fenton, Gordon A.

doi:10.3208/sandf.50.343

Cited by 172 publications

(42 citation statements)

References 27 publications

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“…Geotechnical parameters are often assumed to follow a lognormal distribution as they cannot have negative values [43]. Assuming no correlations between the parameters in prior distribution, the prior distribution is expressed as…”

Section: Bayesian Approachmentioning

confidence: 99%

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Liu

Luo

Huang

et al. 2017

Water

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Abstract:It is important to determine the soil-water characteristic curve (SWCC) for analyzing slope seepage and stability under the conditions of rainfall. However, SWCCs exhibit high uncertainty because of complex influencing factors, which has not been previously considered in slope seepage and stability analysis under conditions of rainfall. This study aimed to evaluate the uncertainty of the SWCC and its effects on the seepage and stability analysis of an unsaturated soil slope under conditions of rainfall. The SWCC model parameters were treated as random variables. An uncertainty evaluation of the parameters was conducted based on the Bayesian approach and the Markov chain Monte Carlo (MCMC) method. Observed data from granite residual soil were used to test the uncertainty of the SWCC. Then, different confidence intervals for the model parameters of the SWCC were constructed. The slope seepage and stability analysis under conditions of rainfall with the SWCC of different confidence intervals was investigated using finite element software (SEEP/W and SLOPE/W). The results demonstrated that SWCC uncertainty had significant effects on slope seepage and stability. In general, the larger the percentile value, the greater the reduction of negative pore-water pressure in the soil layer and the lower the safety factor of the slope. Uncertainties in the model parameters of the SWCC can lead to obvious errors in predicted pore-water pressure profiles and the estimated safety factor of the slope under conditions of rainfall.

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Section: Bayesian Approachmentioning

confidence: 99%

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Liu

Luo

Huang

et al. 2017

Water

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“…Geotechnical parameters are often considered to follow lognormal distributions because they cannot have negative values (Huang et al 2010b). The parameters in Table 2 have been selected to represent a soft clay with coefficients of variance (COV) within the range reported by Baecher and Christian (2003).…”

Section: Random Variables and Their Prior Valuesmentioning

confidence: 99%

Bayesian updating for one-dimensional consolidation measurements

Kelly

Huang

2015

Can. Geotech. J.

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After a geotechnical design has been developed, it is common to monitor performance during construction using the observational method by Peck (published in 1969). The observational method is a process where data are collected and geotechnical models updated, allowing timely decisions to be made with respect to risk and opportunity by asset owners or contractors. The observational method is similar to the mathematical formulation for Bayesian updating of material parameters based on measurements. A proof of concept study has been performed to assess the potential for Bayesian updating to be combined with the observational method to allow timely and accurate decision-making during construction of embankments on soft soils. The method was able to converge to an accurate solution prior to 50% consolidation assuming small measurement errors. It is also demonstrated that confidence in the predicted settlement is relatively low at the prior "design" stage and rapidly increases with three or four measurements spaced over time during the posterior "construction" phase.Key words: statistical analysis, Bayesian probability analysis, embankments.Résumé : Après avoir réalisé une étude de conception géotechnique, on mesure habituellement les performances durant la phase de construction à l'aide de la méthode observationnelle de Peck (publiée en 1969). Celle-ci consiste en un processus durant lequel des données sont recueillies et les modèles géotechniques mis à jour, ce qui permet la prise rapide de décisions par les propriétaires et entrepreneurs. La méthode observationnelle est similaire à la formulation mathématique d'adaptation bayésienne des paramètres de matériau basée sur des mesures. Une étude de validation de principe a été effectuée pour évaluer la capacité de l'adaptation bayésienne à être combinée à la méthode observationnelle dans le but permettre la prise rapide et précise de décisions lors de la construction de talus sur sols mous. La méthode a été en mesure de déboucher sur une solution précise avant la consolidation à 50 %, en supposant la présence de petites erreurs de mesure. On a également démontré que le degré de confiance accordé à la prédiction de tassement est relativement faible lors de l'étape préalable de « conception » et qu'il augmente rapidement en présence de trois ou quatre mesures espacées dans le temps durant la phase ultérieure de « construction ». [Traduit par la Rédaction]Mots-clés : analyse statistique, analyse bayésienne de probabilité, construction de talus.

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“…A polar co-ordinate defined search space is used instead of the more traditional Cartesian co-ordinate system, as this has been shown to be less sensitive to local discontinuities on the limit state surface (Val et al, 1996). As most slopes have multiple viable slip surfaces, the overall failure probability of the slope as a system should be evaluated, not just that of the critical failure surface; the probability of failure of the critical failure surface may be significantly less than that of the slope as a whole (Oka & Wu, 1990;Chowdhury & Xu, 1995;Huang et al, 2010;Zhang et al, 2011;Ji & Low, 2012). Therefore, this paper considers the correlation between different viable slip surfaces and uses that correlation to determine upper and lower bounds for the system's probability of failure.…”

Section: Introductionmentioning

confidence: 99%

“…Over recent years with advances in computing power there has been renewed interest in simulation methods, with numerous authors proposing quasi Monte Carlo methods (Malkawi et al, 2001;Wang et al, 2011;Cheng et al, 2015) as a means of finding the critical probabilistic slip surface. Simulation techniques can be used to find both the system reliability (Griffiths & Fenton, 2004;Huang et al, 2010) and the reliability index for a given slip surface. However, this paper proposes an alternative approximate technique for rapidly finding the system reliability of a slope with multiple failure mechanisms.…”

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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System Reliability of Slopes by RFEM

Cited by 172 publications

References 27 publications

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Bayesian updating for one-dimensional consolidation measurements

System reliability of slopes using multimodal optimisation

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