Discussion: An extension of Bishop's simplified method of slope stability analysis to three dimensions

Géotechnique ◽  
1988 ◽  
Vol 38 (1) ◽  
pp. 155-156 ◽  
Author(s):  
O. Hungr
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Slope Stability Analysis ◽  
Three Dimensions ◽  
Simplified Method

Evaluation of a three-dimensional method of slope stability analysis

1989 ◽  
Vol 26 (4) ◽  
pp. 679-686 ◽  
Author(s):  
Oldrich Hungr ◽  
F. M. Salgado ◽  
P. M. Byrne
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Limit Equilibrium ◽  
Three Dimensional ◽  
Slope Stability Analysis ◽  
Equilibrium Analysis ◽  
Equilibrium Solutions ◽  
Good Correspondence ◽  
Limit Equilibrium Analysis ◽  
Simplified Method

A study comparing a three-dimensional extension of the Bishop simplified method with other limit equilibrium solutions is presented. Very good correspondence is found in cases of rotational and symmetric sliding surfaces, such as ellipsoids. The Bishop method tends to be conservative when applied to nonrotational and asymmetric surfaces because it neglects internal strength. The error is, however, tolerably small for many commonly occurring slide geometries. Indices are proposed to identify cases for which the method should not be used. With its limitations defined, the Bishop simplified method offers a useful algorithm for three-dimensional limit equilibrium analysis. Key words: three-dimensional slope stability analysis.

scholarly journals Evaluation of Deep Learning against Conventional Limit Equilibrium Methods for Slope Stability Analysis

2021 ◽  
Vol 11 (13) ◽  
pp. 6060
Author(s):  
Behnam Azmoon ◽  
Aynaz Biniyaz ◽  
Zhen (Leo) Liu
Keyword(s):  
Deep Learning ◽  
Stability Analysis ◽  
Slope Stability ◽  
Limit Equilibrium ◽  
Slope Stability Analysis ◽  
Learning Models ◽  
Limit Equilibrium Methods ◽  
Simplified Method ◽  
Conventional Methods ◽  
Conventional Limit

This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods.

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