Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach View Full Text


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Article Info

DATE

2015-03-17

AUTHORS

Mohsen Hajihassani, Danial Jahed Armaghani, Masoud Monjezi, Edy Tonnizam Mohamad, Aminaton Marto

ABSTRACT

Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting. More... »

PAGES

2799-2817

References to SciGraph publications

  • 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
  • 2010-08-11. Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach in ARABIAN JOURNAL OF GEOSCIENCES
  • 2014-03-14. Flyrock in bench blasting: a comprehensive review in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2014-09-04. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 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
  • 2015-01-30. Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2005-06. An intelligent approach to prediction and control ground vibration in mines in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2009-10-07. Prediction of blast-induced air overpressure using support vector machine in ARABIAN JOURNAL OF GEOSCIENCES
  • 2010-05-01. Prediction of environmental impacts of quarry blasting operation using fuzzy logic in ENVIRONMENTAL MONITORING AND ASSESSMENT
  • 2015-02-18. Prediction of seismic slope stability through combination of particle swarm optimization and neural network in ENGINEERING WITH COMPUTERS
  • 2011-09-08. Bench blasting design based on site-specific attenuation formula in a quarry in ARABIAN JOURNAL OF GEOSCIENCES
  • 1997-10. A hierarchical analysis for rock engineering using artificial neural networks in ROCK MECHANICS AND ROCK ENGINEERING
  • 2012-02-18. Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network in NEURAL COMPUTING AND APPLICATIONS
  • 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/s12665-015-4274-1

    DOI

    http://dx.doi.org/10.1007/s12665-015-4274-1

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