Журналы →  Eurasian mining →  2018 →  №2 →  Назад

Название Integrated monitoring of engineering structures in mining
DOI 10.17580/em.2018.02.05
Автор Cheskidov V. V., Lipina A. V., Melnichenko I. A.
Информация об авторе

National University of Science and Technology — MISIS, Moscow, Russia^

Cheskidov V. V., Associate Professor, vcheskidov@misis.ru
Lipina A. V., Assistant
Melnichenko I. A., Post-Graduate Student


Technological development in mining calls for accurate modeling and continuous awareness of the behavior of engineering objects in order to ensure mine safety. The information technologies and Big Data enable the next-level design, operation and management in the mining industry. The principal tool of acquisition of information on the parameters and behavior of basic production systems under operation is monitoring. Modern mines are the complex nature-and-technology systems, and their control is impossible without full and reliable information on the behavior of the components. The most hazardous structures in open pit mining are slopes of pit walls, dumps and hydraulic fill dams. The slope stability is governed by a set of factors (geology, hydrogeology, technology and other) which feature high variability in space and time. The ground water level monitoring in a slope structure allows interactive assessment of the slope behavior through calculation of safety factor using geomechanical models of the slope. In the course of time, as a result of variation in operating conditions in the mining waste storage areas, or due to environmental changes (extra moistening of clay rocks, thawing of frozen soil, etc.), physical and mechanical properties of stock piles and their bottoms alter. This fact dictates periodic determination of basic characteristics of slopes: density, cohesion and internal friction angle; the latter, together with the landslide body geometry and hydrogeological conditions govern the ratio of shearing and retaining forces. The optimized-rate measurement of water levels in an aquifer and adjustment of physical and mechanical properties of rocks by means of testing or statistical checking enables reduction in total operating cost and ensures ecological and production safety. Thus, monitoring of engineering objects in mining is the most critical Big Data tool in practical mining upon transition to the technologies of the Fourth Industrial Revolution.

The study was supported by the Russian Foundation for Basic Research, Project No. 16-35-60116 mol_а_dk.

Ключевые слова Mineral mining, IT in mining, Big Data, monitoring, slope, information processing methods, neural networks
Библиографический список

1. Galperin A. M., Kirichenko Yu. V., Kutepov Yu. I. Integrated approach to eco-friendly mining waste management. Gornaya promyshlennost. 2011. No. 5(99). pp. 22–23.
2. Hamza G., Ergun E. A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey. Engineering Geology. 2007. Vol. 93, No. 3–4. pp. 65–81.
3. Manevich A. I., Tatarinov V. N. Application of artificial neural networks to predict modern movements of the Earth’s crust. Issled. Geoinform.: Trudy Geofiz Tsentra RAN. 2017. Vol. 5, No. 2. pp. 37–48. DOI: 10.2205/2017BS045.
4. Corominas J. et al. Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment. 2014. Vol. 74, No. 2. pp. 209–263.
5. Osipov V. L. Delineation of ore occurrence in ore reserves appraisal with MicroMine package. Gornyy Zhurnal. 2015. No. 4. pp. 82–87. DOI: 10.17580/gzh.2015.04.15.
6. Benni T., Rainer B., Thomas G., Stefan J., Malcolm A., Liz H., A WebGIS decision-support system for slope stability based on limit-equilibrium modeling. Engineering Geology. 2013. Vol. 158. pp. 109–118.
7. Cheskidov V. V. Prospects of cad using at engineering-geological pioneering at open cutting. Gornyy informatsionno-analiticheskii byulleten. 2011. No. 11. pp. 355–361.
8. Galperin A. M., Punevskii S. A., Borodina Yu. V., Zung B. K. Development of methods and means for hydro–geomechanical monitoring of slopes. Marksheideriya i nedropollzovanie. 2015. No. 3(77). pp. 22–30.
9. Wang Y., Cao Z., Li D. Bayesian perspective on geotechnical variability and site characterization. Engineering Geology. 2016. Vol. 203. pp. 117–125.
10. Napolskikh S. A., Kryuchkov A. V., Andrievskii A. I., Cheskidov V. V. Remote stability control of hydraulic inwash structures at Stoilensky Mining and Processing Plant. Gornyy Zhurnal. 2017. No. 10. pp. 52–55. DOI: 10.17580/gzh.2017.10.11.
11. Cheskidov V. V., Hydro–geomechanical monitoring of slope structures. Gornaya promyshlennost. 2017. No. 4 (134). 78 p.
12. Behnam Y. B., Danial J. A., Mohd For M. A. Strength characterisation of shale using Mohr–Coulomb and Hoek–Brown criteria. Measurement. 2015. Vol. 63. pp. 269–281.

Language of full-text английский
Полный текст статьи Получить