Search of the Most Dangerous in Slide Slope Stability Analysis Based on Genetic Algorithm
Ke Wang, Chang Ming Wang, Fang Qi, Cen Cen Niu
Abstract
Ke Wang, Chang Ming Wang, Fang Qi, Cen Cen Niu
Abstract
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.
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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.
Key concepts: Slope stability analysis, Slope stability, Safety factor, Stability (learning theory), Slip (aerodynamics), Genetic algorithm, Convergence (economics), Limit (mathematics)