System reliability analysis of slope stability using generalized Subset Simulation

Li, Dianqing; Yang, Zhiyong; Cao, Zi-Jun; Au, Siu‐Kui; Phoon, Kok‐Kwang

doi:10.1016/j.apm.2017.01.047

Cited by 69 publications

(21 citation statements)

References 53 publications

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“…Because of the large production scale and high mining efficiency, open-pit mining is generally adopted in the mining areas with open-pit mining conditions because of its large production scale and high mining efficiency. The boundary of each open-pit is composed of steep slopes of different forms, the slope address conditions are very complex, slope stability has been one of the major public relations issues in the mining industry, and has been widely concerned by the relevant scholars at home and abroad [1][2][3][4][5]. In literature [6], fuzzy neural network is used to evaluate slope stability, in literature [7] grey relational analysis and analytic hierarchy process are applied to slope stability, in literature [8] support vector machine is used.…”

Section: Introductionmentioning

confidence: 99%

A Research on the Methods for Prediction of the Slope Stability of Open-pit Mine

Feng¹,

Guo²,

Li³

2018

Proceedings of the 9th China-Russia Symposium “Coal in the 21st Century: Mining, Intelligent Equipment and Environment Protecti

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In order to improve the slope stability of open-pit mine, this paper proposed four prediction methods BP (Back Propagation)Neural Network, Naive Bayes Classifier, Decision Tree and Support Vector Machine for predicting the classification of slope stability of open-pit mine.Firstly, the sample data of slope stability in open-pit mine are preprocessed, and the new sample data are obtained after data standardization, discretization and attribute reduction. Then, the corresponding prediction model is established by selecting different methods. All the four methods have been successfully applied to the prediction of 8 groups of samples to be tested. In order to determine the optimal method, the detailed accuracy and node error rate are compared to analyze the prediction results. The research shows that the BP neural network has high reliability and good practicability in the evaluation of the slope stability of open-pit mine.

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Section: Introductionmentioning

confidence: 99%

A Research on the Methods for Prediction of the Slope Stability of Open-pit Mine

Feng¹,

Guo²,

Li³

2018

Proceedings of the 9th China-Russia Symposium “Coal in the 21st Century: Mining, Intelligent Equipment and Environment Protecti

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show abstract

“…Efficient slope stability evaluation is important because of the immense human and economic damage caused by landslides around the world every year. A number of methods of evaluating slope stability, ranging from deterministic methods to probabilistic methods, have been developed by engineers and researchers 1–8 . Probabilistic methods are particularly attractive, as, in contrast to deterministic methods, they can account for uncertainties involved in slope stability evaluation, such as measurement error, the inherent spatial variabilities of soil properties, and translation uncertainties between design parameters and measured data 6,9–11 …”

Section: Introductionmentioning

confidence: 99%

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

Zhu

Hiraishi

Pei

et al. 2020

Num Anal Meth Geomechanics

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Efficient evaluation of slope stability is a frontier in geo‐disaster prevention fields. While many slope stability evaluation methods, ranging from deterministic to probabilistic, have been proposed, reliability methods are particularly advantageous as they can account for uncertainties during slope stability evaluation. To address the problem of the low efficiency of direct Monte Carlo simulations and overcome the defects of the traditional response surface method, in this work, a novel probabilistic procedure that integrates a Gaussian process regression‐based surrogate model and the random limit equilibrium method for slope stability evaluation while accounting for the spatial variability of soil strength is proposed. The novel Gaussian process regression‐based surrogate model is efficiently used in the Monte Carlo simulation to reduce the number of calls for direct stability analysis of a slope with spatially varied soil strength. To verify the accuracy and efficiency of the proposed procedure, it was applied to three case studies: the first one is a slope in saturated clay under undrained conditions; the second one is a slope for which the friction angle and cohesion values are cross‐correlated; and the third one is a real slope with multiple soil layers. The results obtained from the comparisons with other methods confirmed the precision and feasibility of the proposed procedure.

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“…Proposed for and applied to landslide prediction, slope reliability analysis considers uncertainties and gives a probabilistic estimate of the slope stability . Juang et al, Phoon and Kulhawy, Griffiths and Fenton, and Li et al have actively promoted the study of risk and reliability in geotechnical engineering. The response surface method (RSM) is commonly used to analyze slope reliability and has seen much development recently.…”

Section: Introductionmentioning

confidence: 99%

An intelligent response surface method for analyzing slope reliability based on Gaussian process regression

Zhu

Pei

Yang

2019

Num Anal Meth Geomechanics

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Summary Problems in geotechnical engineering inevitably involve many uncertainties in the analysis. Reliability methods are important for evaluating slope stability and can take the uncertainties into consideration. In this paper, a novel intelligent response surface method is proposed in which a machine learning algorithm, namely Gaussian process regression, is used to approximate the high‐dimensional and highly nonlinear response hypersurface. An iterative algorithm is also proposed for updating the response surface dynamically by adding the new training point nearest to the limit state surface to the initial training database at each step. The proposed Gaussian process response surface method is used to analyze three different case studies to assess its validity and efficiency. Direct Monte Carlo simulation is also carried out in each case to serve as the benchmark. Comparing with other methods confirms the accuracy and efficiency of the novel intelligent response surface method, which requires fewer performance function calls and avoids the need to normalize the correlative non‐normal variables.

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System reliability analysis of slope stability using generalized Subset Simulation

Cited by 69 publications

References 53 publications

A Research on the Methods for Prediction of the Slope Stability of Open-pit Mine

A Research on the Methods for Prediction of the Slope Stability of Open-pit Mine

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

An intelligent response surface method for analyzing slope reliability based on Gaussian process regression

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