Generalised framework of limit equilibrium methods for slope stability analysis

Zhu, Da; Lee, C. F.; Jiang, H. D.

doi:10.1680/geot.53.4.377.37322

Cited by 30 publications

(26 citation statements)

References 13 publications

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“…The Janbu corrected method is one of the limited equilibrium methods, a group of methods most often used for landslide modelling (Sjöberg, 1996). As shown by Zhu et al (2003) variance of the calculated factor of safety of a slope is relatively small (ca. 1.3) if rigorous methods-like the Janbu corrected method-are used.…”

Section: Methodsmentioning

confidence: 97%

Slope stability and landslides in proglacial varved clays of western Estonia

et al. 2009

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Section: Methodsmentioning

confidence: 97%

Slope stability and landslides in proglacial varved clays of western Estonia

et al. 2009

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“…The limit equilibrium methods are the most often used for landslide modelling (Sjöberg, 1996). As shown by Zhu et al (2003), variance of the calculated factors of safety (FS) of a slope is relatively small (ca 1.3) if rigorous methods satisfying both force and momentum equilibrium are applied. The potential failure surface with the minimum factor of safety was identified with the grid search option.…”

Section: Methodsmentioning

confidence: 99%

Retrogressive slope failure in glaciolacustrine clays: Sauga landslide, western Estonia

et al. 2010

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“…The Simplified Bishop method (1955) was chosen due to its simplicity which makes it easier for this application. In the Simplified Bishop method (1955), it was assumed that the failure surface is represented by a circular arch, which has a center represented by O and a radius represented by R (Zhu et al, 2003). The same slope stability parameters as used in the numerical simulation cohesion, friction angle, unit weight, young modulus and poison ratio has been used for ANN model and validated with numerical model analyzed factor of safety.…”

Section: Back-propagation (Bp) Neural Networkmentioning

confidence: 99%

Landslide Susceptibility Analysis Using Numerical and Neural Network, Near Kedarnath, Uttarakhand, India

Singh

Ansari

Vishal

et al. 2020

American Journal of Environmental Sciences

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The major concern in hilly regions is the stability of those slopes, which have been proclaimed due to unplanned excavation and uneven blasting during road widening and development activity. These slopes again become more vulnerable under dynamic loading and/or various types of human involvement, heavy rainfall and seismic activity. Failure of these slopes leads to loss of property and human being, disruption of traffics and environmental degradation. The Kedarnath area is the most vulnerable hilly terrain due to its inferred locality. To analyze the vulnerability near Kedarnath, the field observation was done to collect the geological and geotechnical details of three vulnerable locations. The present article illustrates the collective analysis of numerical simulation and artificial intelligence (ANN) models for the chosen vulnerable soil slopes. Numerical modeling was done to compute safety factor, stress distribution and maximum displacement using LEM and FEM modules. Further, the machine learning technique, ANN was also functionate to predict the stability based on geotechnical data's and numerical simulation results. The numerical analysis for the homogenous finite slopes shows that slopes are stable, critically stable and also prone to failure during rainy season. The ANN model evaluate that, the FoS by numerical modeling displays 98% validation to predictive neural networking system. The simulation result could be effectively applied to lessen/decrease the effect of regularity for the landslides in the area of particular morphology.

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Generalised framework of limit equilibrium methods for slope stability analysis

Cited by 30 publications

References 13 publications

Slope stability and landslides in proglacial varved clays of western Estonia

Slope stability and landslides in proglacial varved clays of western Estonia

Retrogressive slope failure in glaciolacustrine clays: Sauga landslide, western Estonia

Landslide Susceptibility Analysis Using Numerical and Neural Network, Near Kedarnath, Uttarakhand, India

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