Журналы →  Gornyi Zhurnal →  2025 →  №2 →  Назад

AUTOMATION
Название Improving drilling and blasting design based on artificial intelligence expert systems
DOI 10.17580/gzh.2025.02.07
Автор Khakulov V. A., Karpova Zh. V., Khatukhova D. V., Shinakhova A. E.
Информация об авторе

Kabardino-Balkarian State University, Nalchik, Russia

V. A. Khakulov, Head of Department, Doctor of Engineering Sciences, Professor, vkh21@yandex.ru
Zh. V. Karpova, Candidate of Engineering Sciences
D. V. Khatukhova, Senior Lecturer
A. E. Shinakhova, Master’s Student

Реферат

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.

Ключевые слова Automated mass blast design, rock mass zoning, rock mass breakage, expert systems, project blasting perimeter, drilling and blasting design
Библиографический список

1. Klebanov A. F. Automation and robotization in opencast mining: experience in digital transformation. Gornaya Promyshlennost. 2020. No. 1. pp. 8–11.
2. Khazin M. L. Autonomous mining dump trucks. Izvestiya Uralskogo gosudarstvennogo gornogo universiteta. 2020. No. 3(59). pp. 123–130.
3. Makharatkin P. N., Abdulaev E. K., Vishnyakov G. Yu., Botyan E. Yu., Pushkarev A. E. Increase of efficiency of dump trucks functioning on the basis of justification of their rational speed by means of simulation modeling. MIAB. 2022. No. 6-2. pp. 237–250.
4. Blom M., Pearce A. R., Stuckey P. J. Short-term planning for open pit mines: a review. International Journal of Mining, Reclamation and Environment. 2019. Vol. 33, Iss. 5. pp. 318–339.
5. Nehring M., Knights P. F., Kizil M. S., Hay E. A comparison of strategic mine planning approaches for in-pit crushing and conveying, and truck/shovel systems. International Journal of Mining Science and Technology. 2018. Vol. 28, Iss. 2. pp. 205–214.
6. Upadhyay S. P., Askari-Nasab H. Simulation and optimization approach for uncertaintybased short-term planning in open pit mines. International Journal of Mining Science and Technology. 2018. Vol. 28, Iss. 2. pp. 153–166.
7. Paventi M., Lizotte Y., Scoble M., Mohanty B. Measuring rock mass damage in drifting. Rock Fragmentation by Blasting : Proceedings of the 5th International Symposium on Rock Fragmentation by Blasting. Rotterdam : A.A. Balkema, 1996. pp. 131–138.
8. Wang Q., Gao H., Yu H., Jiang B., Liu B. Method for measuring rock mass characteristics and evaluating the grouting-reinforced effect based on digital drilling. Rock Mechanics and Rock Engineering. 2019. Vol. 52, Iss. 3. pp. 841–851.
9. Phoon K.-K., Shuku T. Future of Machine Learning in Geotechnics (FOMLIG), 5–6 Dec 2023, Okayama, Japan. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 2024. Vol. 18, Iss. 1. pp. 288–303.
10. Zhang W., Gu X., Hong L., Han L., Wang L. Comprehensive r eview of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges. Applied Soft Computing. 2023. Vol. 136. ID 110066.
11. Phoon K.-K., Zhang W. Future of machine learning in geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. 2023. Vol. 17, Iss. 1. pp. 7–22.
12. Kutuzov B. N., Grebenkov Yu. A., Gorodetskiy D. V., Tukachev B. V. Mechanisms of loss of blastholes in open pit apatite mines. Gornyi Zhurnal. 1986. No. 11. pp. 52–54.
13. Khakulov V. A., Shapovalov V. A., Ignatov V. N., Ignatov M. V., Karpova Zh. V. Improving methodology of monitoring geomechanical behavior and property transformation in rock mass toward safe and efficient geotechnology. MIAB. 2023. No. 9. pp. 68–83.
14. Kovalenko V. A., Tangaev I. A. Energy input as a universal production criterion. Advanced Technologies in Surface Mines : Collected Papers. Bishkek, 2015. pp. 5–10.
15. Khakulov V. A., Shapovalov V. A., Ignatov V. N., Ignatov M. V., Nogerov I. A. Improvement of monitoring and management of the roller cone drilling process as part of a self-developing system of rock mass zoning. MIAB. 2023. No. 5, Special issue 1. pp. 3–19.
16. Kovalenko V. A., Dolgushev V. G. Automated drilling and blasting design in open pit mines. Advanced Technologies in Surface Mines of KRSU : Collected Papers. Bishkek, 2008. pp. 82–91.

Language of full-text русский
Полный текст статьи Получить
Назад