Importance and Sensitivity of Variables Defining the Performance of Pre-split Blasting Using Artificial Neural Networks - Mining, Metallurgy & Exploration (2021)

- Organization:
- Society for Mining, Metallurgy & Exploration
- Pages:
- 13
- File Size:
- 3869 KB
- Publication Date:
- May 30, 2021
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.
Citation
APA:
(2021) Importance and Sensitivity of Variables Defining the Performance of Pre-split Blasting Using Artificial Neural Networks - Mining, Metallurgy & Exploration (2021)MLA: Importance and Sensitivity of Variables Defining the Performance of Pre-split Blasting Using Artificial Neural Networks - Mining, Metallurgy & Exploration (2021). Society for Mining, Metallurgy & Exploration, 2021.