Suggestions for slope stability calculations

Chugh, Ashok K.; Smart, John D.

doi:10.1016/0045-7949(81)90082-1

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…Both methods assume that the soil is incompressible and that its properties are homogeneous and isotropic [Chen, 1975]. These methods have been adapted from their original formulations to allow a sloping ground surface and cohesive soil by Chugh and Smart [1981], in the Coulomb case, and by Soubra and Macuh [2002], in the log-spiral case.…”

Section: A Three-dimensional Slope Stability Modelmentioning

confidence: 99%

A spectral clustering search algorithm for predicting shallow landslide size and location

et al. 2015

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The potential hazard and geomorphic significance of shallow landslides depend on their location and size. Commonly applied one-dimensional stability models do not include lateral resistances and cannot predict landslide size. Multidimensional models must be applied to specific geometries, which are not known a priori, and testing all possible geometries is computationally prohibitive. We present an efficient deterministic search algorithm based on spectral graph theory and couple it with a multidimensional stability model to predict discrete landslides in applications at scales broader than a single hillslope using gridded spatial data. The algorithm is general, assuming only that instability results when driving forces acting on a cluster of cells exceed the resisting forces on its margins and that clusters behave as rigid blocks with a failure plane at the soil-bedrock interface. This algorithm recovers predefined clusters of unstable cells of varying shape and size on a synthetic landscape, predicts the size, location, and shape of an observed shallow landslide using field-measured physical parameters, and is robust to modest changes in input parameters. The search algorithm identifies patches of potential instability within large areas of stable landscape. Within these patches will be many different combinations of cells with a Factor of Safety less than one, suggesting that subtle variations in local conditions (e.g., pore pressure and root strength) may determine the ultimate form and exact location at a specific site. Nonetheless, the tests presented here suggest that the search algorithm enables the prediction of shallow landslide size as well as location across landscapes.

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Section: A Three-dimensional Slope Stability Modelmentioning

confidence: 99%

A spectral clustering search algorithm for predicting shallow landslide size and location

et al. 2015

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“…Their slope‐parallel component acts as a driving or resisting force on the margin, while their slope‐normal component modifies the resisting force on the base. The active force on the upslope margin of the group of columns is calculated from vertical stress using the Coulomb active earth pressure coefficient ( K a ), which assumes a planar failure surface [ Chugh and Smart , ]. The passive force on the downslope margin is calculated using the Log‐Spiral passive earth pressure coefficient ( K p ), which accounts for the curvature of the failure surface that develops on this margin [ Soubra and Macuh , ].…”

Section: Slope Stability Modelmentioning

confidence: 99%

Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

et al. 2015

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract Predicting shallow landslide size and location across landscapes is important for understanding landscape form and evolution and for hazard identification. We test a recently developed model that couples a search algorithm with 3-D slope stability analysis that predicts these two key attributes in an intensively studied landscape with a 10 year landslide inventory. We use process-based submodels to estimate soil depth, root strength, and pore pressure for a sequence of landslide-triggering rainstorms. We parameterize submodels with field measurements independently of the slope stability model, without calibrating predictions to observations. The model generally reproduces observed landslide size and location distributions, overlaps 65% of observed landslides, and of these predicts size to within factors of 2 and 1.5 in 55% and 28% of cases, respectively. Five percent of the landscape is predicted unstable, compared to 2% recorded landslide area. Missed landslides are not due to the search algorithm but to the formulation and parameterization of the slope stability model and inaccuracy of observed landslide maps. Our model does not improve location prediction relative to infinite-slope methods but predicts landslide size, improves process representation, and reduces reliance on effective parameters. Increasing rainfall intensity or root cohesion generally increases landslide size and shifts locations down hollow axes, while increasing cohesion restricts unstable locations to areas with deepest soils. Our findings suggest that shallow landslide abundance, location, and size are ultimately controlled by covarying topographic, material, and hydrologic properties. Estimating the spatiotemporal patterns of root strength, pore pressure, and soil depth across a landscape may be the greatest remaining challenge.

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“…This technique is 58 fast and allows the field or emergency engineer to make timely decisions. Although this 59 methodology is only available in some current software (Slide V 5.0, STB 2010, Geo-Slope), and 60 based on limit equilibrium methods, it is highly recommended because of its reliability for 61 representing real conditions in the field [Chugh, 1981]. While these software programmes can 62 reproduce the actual water table, they cannot reproduce the effect of matrix suction caused 63 by rain infiltration [Herrada et al, 2014].…”

Section: Introduction 33mentioning

confidence: 99%

Development and validation of the Terrain Stability model for assessing landslide risk during heavy rain infiltration

Gutiérrez-Martín¹,

Herrada²,

Gallego³

et al. 2018

Preprint

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14Slope stability is a key topic, not only for engineers but also for politicians, due to the 15 considerable monetary and human losses that landslides can cause every year. In fact, it is 16 estimated that landslides have caused thousands of deaths and economic losses amounting to 17 tens of billions of euros per year around the world. The geological stability of slopes is affected 18 by several factors, such as climate, earthquakes, lithology and rock structures, among others. 19Climate is one of the main factors, especially when large amounts of rainwater are absorbed in 20 short periods of time. Taking into account this issue, we developed an innovative analytical 21 model using the limit equilibrium method supported by a geographic information system (GIS

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Suggestions for slope stability calculations

Cited by 12 publications

References 4 publications

A spectral clustering search algorithm for predicting shallow landslide size and location

A spectral clustering search algorithm for predicting shallow landslide size and location

Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

Development and validation of the Terrain Stability model for assessing landslide risk during heavy rain infiltration

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