Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm View Full Text


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Article Info

DATE

2016-08-22

AUTHORS

Roohollah Shirani Faradonbeh, Danial Jahed Armaghani, Hassan Bakhshandeh Amnieh, Edy Tonnizam Mohamad

ABSTRACT

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock. More... »

PAGES

269-281

References to SciGraph publications

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  • 2002. Gene Expression Programming in Problem Solving in SOFT COMPUTING AND INDUSTRY
  • 2016-04-11. Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction in INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • 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
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  • 2016-04-01. Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2016-04-06. Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques in ENGINEERING WITH COMPUTERS
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  • 2009. Firefly Algorithms for Multimodal Optimization in STOCHASTIC ALGORITHMS: FOUNDATIONS AND APPLICATIONS
  • 2015-04-25. Application of two intelligent systems in predicting environmental impacts of quarry blasting in ARABIAN JOURNAL OF GEOSCIENCES
  • 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
  • 2008-07-05. Estimating Rock Cuttability using Regression Trees and Artificial Neural Networks in ROCK MECHANICS AND ROCK ENGINEERING
  • 2015-09-12. Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting in ENGINEERING WITH COMPUTERS
  • 2009-10-19. Firefly Algorithm, Lévy Flights and Global Optimization in RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVI
  • 2012-04-03. Evaluation of effect of blast design parameters on flyrock using artificial neural networks in NEURAL COMPUTING AND APPLICATIONS
  • 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
  • 2012-04-28. A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks in NEURAL COMPUTING AND APPLICATIONS
  • 2015-03-20. Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods in ENGINEERING WITH COMPUTERS
  • 2011-05-26. Evaluation of flyrock phenomenon due to blasting operation by support vector machine in NEURAL COMPUTING AND APPLICATIONS
  • 2015-12-14. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network in ARABIAN JOURNAL OF GEOSCIENCES
  • 2006-12-02. A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
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    http://scigraph.springernature.com/pub.10.1007/s00521-016-2537-8

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    32 schema:description Abstract The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock.
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    53 determination values
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    55 environmental impacts
    56 equations
    57 expression programming
    58 firefly algorithm
    59 first objective
    60 flyrock
    61 flyrock distance
    62 fragmentation
    63 gene expression programming
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    70 minimization
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