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Vol 238
Pages:
371-375
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RUS ENG
Article

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

Authors:
S. A. Ignatyev1
A. E. Sudarikov2
A. Zh. Imashev3
About authors
  • 1 — Saint-Petersburg Mining University
  • 2 — Saint-Petersburg Mining University
  • 3 — Karaganda State Technical University
Date submitted:
2019-03-13
Date accepted:
2019-05-07
Date published:
2019-08-23

Abstract

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.

Область исследования:
Mining
Keywords:
geomechanics miningpressure borderrock mass rock stressing cluster analysis plastic range of stress neural networks
10.31897/pmi.2019.4.371
Go to volume 238

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