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Importance and Sensitivity of Variables Defining Throw and Flyrock in Surface Blasting by Artificial Neural Network Method


Affiliations
1 CSIR-Central Institute of Mining and Fuel Research, Regional Centre Unit-I, MECL Complex, Seminary Hills, Nagpur 440 006, India
2 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad 826 015, India
 

Rock breakage by explosives is followed by throw or heaving the broken material and occasional flyrock. Heaving is a desired feature of blasting for efficient mucking. However, flyrock is a rock fragment that travels beyond the designated distance from a blast in surface mines, and poses a threat to adjacent habitats. Here, we decipher the importance and sensitivity of the variables and factors used to establish the predictive regime of throw with more emphasis on flyrock. The data collected were modelled using artificial neural network approach. The importance and sensitivity of variables and factors were delineated so that they are in tune with the rationale of the outcome of the blast. A combinatory approach was devised to arrive at minimal variables and factors to reduce the statistical redundancy, and to propose a rational predictive regime for throw and flyrock in surface mines.

Keywords

Artificial Neural Network, Blasting, Flyrock, Throw, Surface Mines.
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  • Importance and Sensitivity of Variables Defining Throw and Flyrock in Surface Blasting by Artificial Neural Network Method

Abstract Views: 381  |  PDF Views: 155

Authors

A. K. Raina
CSIR-Central Institute of Mining and Fuel Research, Regional Centre Unit-I, MECL Complex, Seminary Hills, Nagpur 440 006, India
V. M. S. R. Murthy
Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad 826 015, India

Abstract


Rock breakage by explosives is followed by throw or heaving the broken material and occasional flyrock. Heaving is a desired feature of blasting for efficient mucking. However, flyrock is a rock fragment that travels beyond the designated distance from a blast in surface mines, and poses a threat to adjacent habitats. Here, we decipher the importance and sensitivity of the variables and factors used to establish the predictive regime of throw with more emphasis on flyrock. The data collected were modelled using artificial neural network approach. The importance and sensitivity of variables and factors were delineated so that they are in tune with the rationale of the outcome of the blast. A combinatory approach was devised to arrive at minimal variables and factors to reduce the statistical redundancy, and to propose a rational predictive regime for throw and flyrock in surface mines.

Keywords


Artificial Neural Network, Blasting, Flyrock, Throw, Surface Mines.

References





DOI: https://doi.org/10.18520/cs%2Fv111%2Fi9%2F1524-1531