Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

An efficient approach for locating the critical slip surface in slope stability analyses using a real-coded genetic algorithm

Li, Yu-Chao, Chen, Yun-Min, Zhan, Tony L.T., Ling, Dao-Sheng and Cleall, Peter John 2010. An efficient approach for locating the critical slip surface in slope stability analyses using a real-coded genetic algorithm. Canadian Geotechnical Journal 47 (7) , pp. 806-820. 10.1139/T09-124

Full text not available from this repository.

Abstract

A real-coded genetic algorithm is employed to develop a search approach for locating the noncircular critical slip surface in slope stability analyses. Limit equilibrium methods and the finite-element-based method are incorporated with the proposed search approach to calculate the factor of safety. Geometrical and kinematical compatibility constraints are established based on the features of slope problems to prevent slip surfaces from being unreasonable. A dynamic bound technique is presented to improve the search performance with more effective exploration within the solution domain. A number of examples are investigated that demonstrate the proposed search approach to be efficient in yielding accurate solutions to practical slope stability problems. The proposed search approach is stable and highly correlated with the results of independent analyses. Furthermore, this paper demonstrates the successful application of a real-coded genetic algorithm to noncircular critical slip surface search problems.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: genetic algorithm, critical slip surface, slope stability analysis, factor of safety
Publisher: NRC Research Press
ISSN: 0008-3674
Last Modified: 04 Jun 2017 04:19
URI: http://orca.cf.ac.uk/id/eprint/36791

Citation Data

Cited 54 times in Google Scholar. View in Google Scholar

Cited 53 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item