Maximum likelihood estimation of slope stability

Sah, NP; Sheorey, P.R.; Upadhyaya, Lakshmi N.

doi:10.1016/0148-9062(94)92314-0

Cited by 93 publications

(50 citation statements)

References 7 publications

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“…There are many methods to estimate the stability of slope such as Limit equilibrium technique (Fellenius 1936, Bishop 1955, Bishop and Morgenstern 1960, Morgenstern and Price 1965, upper bound theorem of limit analysis (Michalowski 1994, 2002 and finite element method (Griffiths and Lane 1999). Sah et al (1994) have used the maximum likelihood method and given an equation for prediction of the factor of safety (FOS) and observed that the value matches well with that obtained using the limit equilibrium method. Yang et al (2004) used genetic programming and also presented an equation for the FOS.…”

Section: Introductionmentioning

confidence: 77%

Support vector classifier analysis of slope

Samui

2013

Geomatics, Natural Hazards and Risk

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This article examines the potential of support vector machine (SVM) in stability status prediction of slope. Support vector machine achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model rather than the minimizing the error on the training data only. This study uses SVM as a classification tool. In this article, the input data for slope stability prediction consist of values of geotechnical and geometric properties of slope. The accuracy of the SVM is 100% for this problem. The developed SVM gives also an equation for prediction of status of slope. This study shows that SVM has the potential to be a useful and practical tool for prediction of slope stability.

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Section: Introductionmentioning

confidence: 77%

Support vector classifier analysis of slope

Samui

2013

Geomatics, Natural Hazards and Risk

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“…These figures are calculated using the data set provided by Sah et al (1994), who collected information on 46 worldwide slope cases. Similarly, F t has been assigned a value of one by Alonso (1976).…”

Section: Empirical Approachmentioning

confidence: 99%

Development of fragility functions for slope instability analysis

2014

Landslides

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This paper presents a methodology for constructing fragility functions to characterise slope stability under a range of catastrophic earthquakes and rainfalls. The procedures for creating fragility functions, including the first-order reliability method (FORM) and the copula-based sampling method (CBSM), are demonstrated using a selection of typical slopes. The most common failure modes are included, such as the shallow sliding of an infinite slope, circular slip surface of a homogeneous slope, and tetrahedral wedge failure in a rock slope. Owing to the proposed approach, the fragility function can be applied to quantify the failure probabilities over a range of loading conditions with ease, as these are attributed to a function, rather than a design point. The advantage of these definitions is that the uncertainties of correlated soil shear strengths can be incorporated into the reliability models. The established procedure can provide a basis for describing vulnerable behaviour of a slope under various loading conditions and geometries.

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“…The second data set consists of 14 case studies of rock slopes analyzed for wedge failure mechanism. All cases are dry (8 failed, 7 stable) (Sah et al, 1994). The original data covering the 46 case studies are presented in Table 1, while the original data covering the 14 case studies are presented in Table 2.…”

Section: Slope Stability Ann Architecture and Data Availabilitymentioning

confidence: 99%

A study of slope stability prediction using neural networks

Sakellariou

Ferentinou²

2005

Geotech Geol Eng

222

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The determination of the non-linear behaviour of multivariate dynamic systems often presents a challenging and demanding problem. Slope stability estimation is an engineering problem that involves several parameters. The impact of these parameters on the stability of slopes is investigated through the use of computational tools called neural networks. A number of networks of threshold logic unit were tested, with adjustable weights. The computational method for the training process was a back-propagation learning algorithm. In this paper, the input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FS) that can be modelled as a function approximation problem, or the stability status (S) that can be modelled either as a function approximation problem or as a classification model. The performance of the network is measured and the results are compared to those obtained by means of standard analytical methods. Furthermore, the relative importance of the parameters is studied using the method of the partitioning of weights and compared to the results obtained through the use of Index Information Theory.

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Maximum likelihood estimation of slope stability

Cited by 93 publications

References 7 publications

Support vector classifier analysis of slope

Support vector classifier analysis of slope

Development of fragility functions for slope instability analysis

A study of slope stability prediction using neural networks

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