Using real-coded genetic algorithm for finding critical factor of safety in slope stability analysis

2004 ◽  
pp. 467-470
Author(s):  
Yuanpai Zhou ◽  
Shouju Li ◽  
Yingxi Liu ◽  
Huiling Sun
Keyword(s):  
Genetic Algorithm ◽  
Stability Analysis ◽  
Slope Stability ◽  
Factor Of Safety ◽  
Critical Factor ◽  
Slope Stability Analysis ◽  
Real Coded Genetic Algorithm

scholarly journals Prediction of Critical Factor of Safety in Soil Slopes Using Adaptive Network-Based Fuzzy Inference System (ANFIS)

2021 ◽  
Author(s):  
Babak
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Fuzzy Inference System ◽  
Factor Of Safety ◽  
Fuzzy Inference ◽  
Critical Factor ◽  
Slope Stability Analysis ◽  
Subtractive Clustering ◽  
Adaptive Network ◽  
Inference System

Slope stability analysis is among the uncertain problems in geotechnical engineering; This is mostly because of the presence of the complex nature of the soil and rock mass and uncertain parameters affecting the problem.To Determine the structure of the model and deal with the complexity of the geotechnical behavior of soil a data approach is required.Previous studies in the literature do not predict slope stability with high accuracy.In this study, to increase the accuracy of prediction of slope stability, the Adaptive Network-Based Fuzzy Inference System (ANFIS) was used. Three clustering methods are used in combination with ANFIS, namely the Grid Partitioning (GP) method, Subtractive Clustering Method (SCM), and Fuzzy C-means Method (FCM) for predicting the critical Factor of Safety (FS). Since the coefficient of determination R^(2 )in the SCM model is closer to 1, the prediction performance of the SCM model outperforms the GP and FCM. The results of the study indicate that ANFIS can provide more accurate predictions compared to other conventional slope stability analysis approaches. ANFIS method efficiently reduced the uncertainties involved in the estimation and analysis of slope stability as compared to previous approaches in the literature.

Extensions to the generalized method of slices for stability analysis

1983 ◽  
Vol 20 (1) ◽  
pp. 104-119 ◽  
Author(s):  
Zu-Yu Chen ◽  
N. R. Morgenstern
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Computer Program ◽  
Factor Of Safety ◽  
Numerical Procedure ◽  
Slope Stability Analysis ◽  
Methods Of Analysis

Extensions are suggested to the generalized method of slices that is commonly used in slope stability analysis. It is shown that restrictions exist on the assumptions used to make the problem statically determinate. In addition, a numerical procedure has been developed to find the bounds to the factor of safety, subject to additional requirements of physical admissibility. As a result of these developments it has been possible to produce a revised computer program that appears to overcome the problems of convergence experienced by other programs in current use. Results obtained with this new analysis confirm the reliability of several methods of analysis used in practice.

Search of the Most Dangerous in Slide Slope Stability Analysis Based on Genetic Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 2535-2538
Author(s):  
Ke Wang ◽  
Chang Ming Wang ◽  
Fang Qi ◽  
Cen Cen Niu
Keyword(s):  
Genetic Algorithm ◽  
Stability Analysis ◽  
Slope Stability ◽  
Slip Surface ◽  
Limit Equilibrium ◽  
Evolutionary Process ◽  
Limit Equilibrium Method ◽  
Slope Stability Analysis ◽  
Equilibrium Method ◽  
The Stability

The traditional limit equilibrium method in the analysis of slope stability not only exists some subjective empirical hypothesis that can not meet the equilibrium of force and moment, but also ignores the effects of internal stress and strain on the slope stability. Furthermore, in the stability of the slope evaluation, limit equilibrium method relies too much on experience when hypothesizing the slope slip surface. So that it makes deviation on slope analysis and stability evaluation. This paper is based on simplified Bishop method used to establish the model of slope stability analysis. And it used genetic algorithms to solve the minimum safety factor and the most dangerous slip surface of slope. It was the arithmetic which simulates organisms genetic evolutionary process and it avoided the traditional methods falling into the local extreme value point easily and error propagation leading to convergence. The algorithm had advantages of higher accuracy, quick convergence and applicability. It showed that the genetic algorithm is accurate and reliable in the analysis of slope stability.

scholarly journals ANALISIS KESTABILAN LERENG DENGAN SOFTWARE ROCSCIENCE SLIDE

2018 ◽  
Author(s):  
Darmadi Ir
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Case Studies ◽  
Safety Factor ◽  
Factor Of Safety ◽  
Slope Angle ◽  
Maximum Load ◽  
Slope Stability Analysis

Abstract Slope stability analysis with SOFTWARE ROCSCIENCE SLIDE case studies in residential barracks of PT. Freport with various variations in loading and conditions show results The greater the load on the slope, the lower the Factor of Safety value. FS values for all methods, sections, and ramp widths are greater in dry than wet conditions. The greater the load distance from the slope, the greater the FS value. At a distance of 3m from the crest slope the decrease in FS value is very significant, in sections 1 and 2 there is still a secure FS value with a load of 50 kN / m, the smaller the overall slope angle (slope) the greater the FS value.Keywords: Slope stability, safety factor, maximum load

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