Locating the general failure surface of earth slope using particle swarm optimisation

Mohammad Khajehzadeh, Mohd. Raihan Taha, Ahmed El-Shafie, Mahdiyeh Eslami

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

In this study, a new particle swarm optimisation (PSO) approach is proposed for evaluating the factor of safety (FS) in a slope stability analysis based on the limit equilibrium method. The safety factors of the general slip surfaces are calculated using a concise algorithm of the Morgenstern-Price method, which satisfies both the force and the moment equilibriums. Each new slip surface is randomly generated by a straight-line technique. The performance of the proposed algorithm is evaluated using a set of three benchmark functions and three slope stability problems from the literature. The results indicate that the new method can provide high-quality, accurate and efficient solutions for computing the FS. Moreover, this method can predict a more critical failure mechanism of earth slope and outperform both the other methods in the literature and the standard PSO.

Original languageEnglish
Pages (from-to)41-57
Number of pages17
JournalCivil Engineering and Environmental Systems
Volume29
Issue number1
DOIs
Publication statusPublished - Mar 2012

Fingerprint

Slope stability
Particle swarm optimization (PSO)
Earth (planet)
Safety factor

Keywords

  • critical failure surface
  • factor of safety
  • limit equilibrium
  • particle swarm optimisation
  • slope stability

ASJC Scopus subject areas

  • Civil and Structural Engineering

Cite this

Locating the general failure surface of earth slope using particle swarm optimisation. / Khajehzadeh, Mohammad; Taha, Mohd. Raihan; El-Shafie, Ahmed; Eslami, Mahdiyeh.

In: Civil Engineering and Environmental Systems, Vol. 29, No. 1, 03.2012, p. 41-57.

Research output: Contribution to journalArticle

Khajehzadeh, Mohammad ; Taha, Mohd. Raihan ; El-Shafie, Ahmed ; Eslami, Mahdiyeh. / Locating the general failure surface of earth slope using particle swarm optimisation. In: Civil Engineering and Environmental Systems. 2012 ; Vol. 29, No. 1. pp. 41-57.
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