Prediction of seismic slope stability through combination of particle swarm optimization and neural network View Full Text


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Article Info

DATE

2015-02-18

AUTHORS

Behrouz Gordan, Danial Jahed Armaghani, Mohsen Hajihassani, Masoud Monjezi

ABSTRACT

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R2) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique. More... »

PAGES

85-97

References to SciGraph publications

  • 2013-03-27. The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
  • 2006-07-07. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models in LANDSLIDES
  • 2012-01-17. Spatial stability of slope cuts in rock massifs using GIS technology and probabilistic analysis in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2011-01-22. Classification of slopes and prediction of factor of safety using differential evolution neural networks in ENVIRONMENTAL EARTH SCIENCES
  • 2014-10-18. An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2009-11-13. Application of soft computing to predict blast-induced ground vibration in ENGINEERING WITH COMPUTERS
  • 2009-02-05. Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran) in ARABIAN JOURNAL OF GEOSCIENCES
  • 2010-08-11. Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach in ARABIAN JOURNAL OF GEOSCIENCES
  • 2009-01-23. A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2005-08. A study of slope stability prediction using neural networks in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2012-07-12. Backbreak prediction in the Chadormalu iron mine using artificial neural network in NEURAL COMPUTING AND APPLICATIONS
  • 2014-07-10. Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 1993. Statistical aspects of neural networks in NETWORKS AND CHAOS — STATISTICAL AND PROBABILISTIC ASPECTS
  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
  • 2006-12. Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia in JOURNAL OF EARTH SYSTEM SCIENCE
  • 2010-03-10. Prediction of rock fragmentation due to blasting using artificial neural network in ENGINEERING WITH COMPUTERS
  • 2007-06. Particle swarm optimization in SWARM INTELLIGENCE
  • 2007-03-02. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s00366-015-0400-7

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