Application of two intelligent systems in predicting environmental impacts of quarry blasting View Full Text


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Article Info

DATE

2015-04-25

AUTHORS

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

ABSTRACT

Blasting, as the most frequently used method for hard rock fragmentation, is a hazardous aspect in mining industries. These operations produce several undesirable environmental impacts such as ground vibration, air-overpressure (AOp), and flyrock in the nearby environments. These environmental impacts may cause injury to human and damage to structures, groundwater, and ecology of the nearby area. This paper is aimed to predict the blasting environmental impacts in granite quarry sites through two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). For this purpose, 166 blasting operations at four granite quarry sites in Malaysia were investigated and the values of peak particle velocity (PPV), AOp, and flyrock were precisely recorded in each blasting operation. Considering some model performance indices including coefficient of determination (R2), value account for (VAF), and root mean square error (RMSE), and also using simple ranking procedure, the best models for prediction of PPV, AOp, and flyrock were selected. The results demonstrated that the ANFIS models yield higher performance capacity compared to ANN models. In the case of testing datasets, the R2 values of 0.939, 0.947, and 0.959 for prediction of PPV, AOp, and flyrock, respectively, suggest the superiority of the ANFIS technique, while in predicting PPV, AOp, and flyrock using ANN technique, these values are 0.771, 0.864, and 0.834, respectively. More... »

PAGES

9647-9665

References to SciGraph publications

  • 2009-11-13. Application of soft computing to predict blast-induced ground vibration in ENGINEERING WITH COMPUTERS
  • 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
  • 1993. Statistical aspects of neural networks in NETWORKS AND CHAOS — STATISTICAL AND PROBABILISTIC ASPECTS
  • 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
  • 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
  • 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
  • 2014-03-20. A typical weathering profile of granitic rock in Johor, Malaysia based on joint characterization in ARABIAN JOURNAL OF GEOSCIENCES
  • 2012-04-03. Evaluation of effect of blast design parameters on flyrock using artificial neural networks in NEURAL COMPUTING AND APPLICATIONS
  • 2012-10-16. Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation in ARABIAN JOURNAL OF GEOSCIENCES
  • 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
  • 2011-05-26. Evaluation of flyrock phenomenon due to blasting operation by support vector machine in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s12517-015-1908-2

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    62 ground vibration
    63 groundwater
    64 hard rock fragmentation
    65 hazardous aspects
    66 higher performance capacity
    67 humans
    68 impact
    69 index
    70 industry
    71 inference system
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    80 nearby environment
    81 network
    82 neural network
    83 neuro-fuzzy inference system
    84 operation
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    87 peak particle velocity
    88 performance capacity
    89 performance index
    90 prediction
    91 prediction of PPV
    92 procedure
    93 purpose
    94 quarry blasting
    95 quarry sites
    96 results
    97 rock fragmentation
    98 root mean square error
    99 sites
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    102 superiority
    103 system
    104 technique
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    107 values
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