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Askar Zh. Imashev
Askar Zh. Imashev
Head of Department, Ph.D., Associate Professor
Abylkas Saginov Karaganda Technical University
Head of Department, Ph.D., Associate Professor
Abylkas Saginov Karaganda Technical University
Kazakhstan
Kazakhstan
114
Total cited
8
Hirsch index

Articles

Geotechnical Engineering and Engineering Geology
  • Date submitted
    2023-03-14
  • Date accepted
    2023-10-25
  • Online publication date
    2024-02-22
  • Date published
    2024-04-25

Predictive assessment of ore dilution in mining thin steeply dipping deposits by a system of sublevel drifts

Article preview

The purpose of research is the study of stress-strain state of marginal rock mass around the stope and predictive assessment of ore dilution with regard for changes in ore body thickness in mining thin ore deposits on the example of the Zholymbet mine. Study of the specific features of the stress-strain state development was accomplished applying the methodology based on numerical research methods taking into account the geological strength index (GSI) which allows considering the structural features of rocks, fracturing, lithology, water content and other strength indicators, due to which there is a correct transition from the rock sample strength to the rock mass strength. The results of numerical analysis of the stress-strain state of the marginal part of the rock mass using the finite element method after the Hoek – Brown strength criterion made it possible to assess the geomechanical state in the marginal mass provided there are changes in ore body thickness and to predict the volume of ore dilution. It was ascertained that when mining thin ore deposits, the predicted value of ore dilution is influenced by the ore body thickness and the GSI. The dependence of changes in ore dilution values on the GSI was recorded taking into account changes in ore body thickness from 1 to 3 m. Analysis of the research results showed that the predicted dimensions of rock failure zone around the stopes are quite large, due to which the indicators of the estimated ore dilution are not attained. There is a need to reduce the seismic impact of the blasting force on the marginal rock mass and update the blasting chart.

How to cite: Imashev A.Z., Suimbaeva A.M., Musin A.A. Predictive assessment of ore dilution in mining thin steeply dipping deposits by a system of sublevel drifts // Journal of Mining Institute. 2024. Vol. 266. p. 283-294. EDN GPKEBJ
Mining
  • Date submitted
    2019-03-13
  • Date accepted
    2019-05-07
  • Date published
    2019-08-25

Modern Mathematical Forecast Methods of Maintenance and Support Conditions for Mining Tunnel

Article preview

The research focuses on mathematical methods of mining pressure forecast to develop rational support patterns for mining tunnels and to ensure safety of mining operations. The purpose of research is to develop the methodology of applying advanced calculation methods and software solutions based on neural networks to reduce dispersion of factors influencing stability of mining tunnels, as well as to define rational parameters of mining tunnel support. The authors review the algorithm of geomechanical process examination, which is divided into several stages. First of all, it is proposed to use cluster analysis to examine location conditions of man-made outcrops, which allows to divide all the diversity of existing conditions for mining tunnel construction. Cluster analysis first allows to reduce the dispersion of factors that influence the stability of mining tunnels in various clusters, and then to determine rational parameters of tunnel support in each cluster. After the problem of cluster analysis is solved, it is proposed to use software programs that allow to study geomechanical processes in each cluster. At this stage, both standard methods (normative techniques, numerical modelling, analogies use, etc.) and the most advanced methods – neural networks – can be applied. Described algorithm of solving geomechanical problems, which utilizes advanced numerical methods and a software package based on neural networks, ensures an individual approach to estimation of mining pressure under varying conditions of man-made outcrop location in the rock mass.

How to cite: Ignatyev S.A., Sudarikov A.E., Imashev A.Z. Modern Mathematical Forecast Methods of Maintenance and Support Conditions for Mining Tunnel // Journal of Mining Institute. 2019. Vol. 238. p. 371. DOI: 10.31897/PMI.2019.4.371