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Importance and Sensitivity of Variables Defining the Performance of Pre-split Blasting Using Artificial Neural Networks

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Abstract

Blast induced damage to the final wall of rockmass in any civil or engineering application is a major concern to the rock excavation engineers. There are at least four distinct techniques practised by blasting engineers to minimise the damage to the parent rock during blasting that are classified as contour blasting. One of the methods to achieve a smooth wall or rock surface in blasting is pre-splitting, where blastholes in the final row are mildly loaded with explosives and fired first in sequence before the rest of the holes in a round of blast to achieve a split in rockmass. Since blasting is one of the most adopted methods for achieving a final wall in a desired shape, while maintaining the rockmass integrity, the design of pre-split blast assumes importance. There are several variables that determine the final shape of the pre-split face including the rock type, the drill diameter, the explosive characteristics, the orientation of major joints with respect to drill hole, the linear charge density, the spacing of drill holes and inclination of the drill holes. This study attempts to determine the importance and sensitivity of variables in pre-split blasting using historical data from open excavations of a hydroelectric project in India. Keeping in view the multitude of variables, low correlation of independent variables with responses and high level of interactions between the variables that define the blast results, it was found suitable to use ANN to determine the importance and sensitivity of the variables involved. A new method to determine the blast induced damage to rockmass, namely, undamaged area (AUD%), is introduced. Half Cast Factor (HCF%) and AUD% were calculated for a high wall and trenches by digital image analysis technique using the Fragalyst software. Joint spacing and linear charge density emerged as most controlling variables in determining the final result of pre-blast. The relative importance of other variables assuming significance in the case of both the designed outputs is also discussed. A comparative analysis of HCF% and AUD% on the basis of results obtained from ANN analysis is also presented. A futuristic approach concept has also been introduced.

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Importance and sensitivity of variables on the performance of pre-split blasting using artificial neural networks

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Notes

  1. https://en.wikipedia.org/wiki/Artificial_neural_network retrieved on 05.04.2019

  2. www.easynn.com/108491/EasyNN-plus.pdf retrieved on 05.04.2019

  3. http://www.easynn.com/inside.htm retrieved on 05.04.2019

Abbreviations

δh :

Blast hole deviation

Sb :

Spacing of blastholes in pre-split (m)

Sj:

Spacing of major influencing joints (m)

IB :

Index of blastability

σtd :

Dynamic tensile strength of the rock in MPa and calculated as function of compressive strength

E:

Modulus of elasticity of the rock (GPa)

ρr :

Density of rock (g/cm 3 )

qlc :

Linear charge concentration of explosive (kg/m)

θ:

Normalised angle of blasthole with respect to major joint in radians

HCF%:

Half Cast Factor

AUD%:

Undamaged area (%)

DIAT:

Digital image analysis technique

ANOVA:

Analysis of variance

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Acknowledgements

Thanks are due to the Director CSIR-Central Institute of Mining and Fuel Research for his permission to publish the paper. Thanks are due to Andra Pradesh Power Generation Corporation for sponsoring the study and Shri Ramesh for his unfailing efforts and help. Help rendered by Suraj and my colleagues at various stages is gratefully acknowledged.

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Raina, A.K. Importance and Sensitivity of Variables Defining the Performance of Pre-split Blasting Using Artificial Neural Networks. Mining, Metallurgy & Exploration 38, 1817–1829 (2021). https://doi.org/10.1007/s42461-021-00435-6

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