Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization View Full Text


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Article Info

DATE

2013-11-27

AUTHORS

D. Jahed Armaghani, M. Hajihassani, E. Tonnizam Mohamad, A. Marto, S. A. Noorani

ABSTRACT

Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding areas. In recent years, several researches have been done to predict flyrock and ground vibration by means of conventional backpropagation (BP) artificial neural network (ANN). However, the convergence rate of the BP-ANN is relatively slow and solutions can be trapped at local minima. Since particle swarm optimization (PSO) is a robust global search algorithm, it can be used to improve ANNs' performance. In this study, a novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN. This approach was applied to simulate the flyrock distance and peak particle velocity (PPV) induced by blasting. PSO parameters and optimal network architecture were determined using sensitivity analysis and trial and error method, respectively. Finally, a model was selected, and the proposed model was trained and tested using 44 datasets obtained from three granite quarry sites in Malaysia. Each dataset involved ten inputs, including the most influential parameters on flyrock distance and PPV, and two outputs. The results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy. Sensitivity analysis was also conducted to determine the influence of each parameter on flyrock distance and PPV. The results show that the powder factor and charge per delay are the most effective parameters on flyrock distance, whereas sub-drilling and charge per delay are the most effective parameters on PPV. More... »

PAGES

5383-5396

References to SciGraph publications

  • 2012-11-23. Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects 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
  • 2007-12-05. Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system in ENVIRONMENTAL EARTH SCIENCES
  • 2005-06. An intelligent approach to prediction and control ground vibration in mines in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2013-02-21. A new damage criteria norm for blast-induced ground vibrations in Turkey in ARABIAN JOURNAL OF GEOSCIENCES
  • 2011-01-07. Burden prediction in blasting operation using rock geomechanical properties in ARABIAN JOURNAL OF GEOSCIENCES
  • 2010-05-01. Prediction of environmental impacts of quarry blasting operation using fuzzy logic in ENVIRONMENTAL MONITORING AND ASSESSMENT
  • 2009-10-30. Prediction and controlling of flyrock in blasting operation using artificial neural network in ARABIAN JOURNAL OF GEOSCIENCES
  • 2012-10-16. Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation in ARABIAN JOURNAL OF GEOSCIENCES
  • 1997-10. A hierarchical analysis for rock engineering using artificial neural networks in ROCK MECHANICS AND ROCK ENGINEERING
  • 2011-09-08. Bench blasting design based on site-specific attenuation formula in a quarry in ARABIAN JOURNAL OF GEOSCIENCES
  • 1999-10. Studies on Flyrock at Limestone Quarries in ROCK MECHANICS AND ROCK ENGINEERING
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