Improving drilling and blasting design based on artificial intelligence expert systems

The article presents a new version of the system of automated mass blasting design in open-pit mining using self-developing expert systems for rock mass zoning based on blastability. When choosing a blastability category, the rational drilling and blasting (DB) design is proposed to be such that conforms with the minimum explosive consumption which ensures quality treatment of bench bottom and minimum destruction of rock mass beyond the blasting perimeter. The expert system addresses two objectives: rock mass zoning adjustment for a standard DB design for a standard structure of rock mass; scaling of DB designs for the actual elements of a mining system and actual structure of rock mass. The development of the data base of the expert model uses the system analysis of the actual results of mining processes and the actual parameters of industrial explosions in conjunction with a test site of rock mass and obtained from the intelligent drill rig–shovel systems. This reduces the influence of subjective factors in generation of initial data in mass blast designs. The use of the expert systems promotes CAD introduction at new mines, and facilitates project implementation and current organization of mining processes in open pits. The result shows up as the reduced explosive consumption in rock breakage owing to the reduced volume of overdrilling and the decreased loss of wells and explosive consumption spent to break rocks beyond the blasting perimeter. It is pointed at the higher volume of rock fragmentation as a result of reduced loss of areas on bench slopes.

Authors

Publication Details

Journal
Gornyi Zhurnal
Published
2025-02-28
DOI
https://doi.org/10.17580/gzh.2025.02.07
Citations
1
Primary Topic
Advanced Computational Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.48
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article

Improving drilling and blasting design based on artificial intelligence expert systems

Zh.V. Karpova, Dana V. Khatukhova, V.A. Khakulov, A. E. Shinakhova
1 citations
Gornyi Zhurnal
Advanced Computational Techniques and Applications
2.48
article

Improving drilling and blasting design based on artificial intelligence expert systems

Zh.V. Karpova, Dana V. Khatukhova, V.A. Khakulov, A. E. Shinakhova
article en
1 citations

Abstract

The article presents a new version of the system of automated mass blasting design in open-pit mining using self-developing expert systems for rock mass zoning based on blastability. When choosing a blastability category, the rational drilling and blasting (DB) design is proposed to be such that conforms with the minimum explosive consumption which ensures quality treatment of bench bottom and minimum destruction of rock mass beyond the blasting perimeter. The expert system addresses two objectives: rock mass zoning adjustment for a standard DB design for a standard structure of rock mass; scaling of DB designs for the actual elements of a mining system and actual structure of rock mass. The development of the data base of the expert model uses the system analysis of the actual results of mining processes and the actual parameters of industrial explosions in conjunction with a test site of rock mass and obtained from the intelligent drill rig–shovel systems. This reduces the influence of subjective factors in generation of initial data in mass blast designs. The use of the expert systems promotes CAD introduction at new mines, and facilitates project implementation and current organization of mining processes in open pits. The result shows up as the reduced explosive consumption in rock breakage owing to the reduced volume of overdrilling and the decreased loss of wells and explosive consumption spent to break rocks beyond the blasting perimeter. It is pointed at the higher volume of rock fragmentation as a result of reduced loss of areas on bench slopes.

Gornyi Zhurnal(2)
Openalex Percentile: Top 11%
Advanced Computational Techniques and Applications
2.48
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