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Development of GP and GEP models to estimate an environmental issue induced by blasting operation

  • Roohollah Shirani Faradonbeh
  • Mahdi HasanipanahEmail author
  • Hassan Bakhshandeh Amnieh
  • Danial Jahed Armaghani
  • Masoud Monjezi
Article

Abstract

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.

Keywords

Blasting Air overpressure Genetic programming Gene expression programming 

Abbreviations

ANN

Artificial neural network

AOp

Air overpressure

CoD

Coefficient of determination

CPs

Computer programs

D

Distance between monitoring station and blast point

EAs

Evolutionary algorithms

ETs

Expression trees

F

Function set

GA

Genetic algorithm

GEP

Gene expression programming

GP

Genetic programming

ICA

Imperialist competitive algorithm

MC

Maximum charge used per delay

MLR

Multiple linear regression

PSO

Particle swarm optimization

RMSE

Root mean square error

T

Terminal set

USBM

US Bureau of Mines

VAF

Variance account for

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Roohollah Shirani Faradonbeh
    • 1
  • Mahdi Hasanipanah
    • 2
    Email author
  • Hassan Bakhshandeh Amnieh
    • 3
  • Danial Jahed Armaghani
    • 4
  • Masoud Monjezi
    • 5
  1. 1.Young Researchers and Elite Club, South Tehran BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Mining EngineeringUniversity of KashanKashanIran
  3. 3.School of Mining, College of EngineeringUniversity of TehranTehranIran
  4. 4.Department of Civil and Environmental EngineeringAmirkabir University of TechnologyTehranIran
  5. 5.Department of Mining EngineeringTarbiat Modares UniversityTehranIran

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