Journals →  Gornyi Zhurnal →  2016 →  #7 →  Back

POWER SYSTEM MANAGEMENT, AUTOMATION
ArticleName Expert system for mathematical modeling of geomechanical processes in rock mass
DOI 10.17580/gzh.2016.07.21
ArticleAuthor Khalkechev R. K.
ArticleAuthorData

National University of Science and Technology MISIS, Moscow, Russia:

R. K. Khalkechev, Associate Professor, Candidate of Physico-Mathematical Sciences, syrus@list.ru

Abstract

Despite considerable advancement of information technologies in the area of mining, the attempts of the modern researchers to create a single universal mathematical model to describe geomechanical processes in rock mass have failed to produce any notable result. It is found that a mathematical model capable to solve wider class of problems exhibits limited abilities in solving individual applied problems. The causes of that are, on the on hand, insolvability of a problem within a universal model endeavoring to cover all characteristics of rocks, and, on the other hand, aliasing of objectives of the modeling (for instance, some problems require stability of preset rock mass areas, while the other problems assign failure in these areas). For this reason, the prevailing trend of mathematical modeling in geomechanics is solution of individual applied problems. Under such circumstances, many theories on mathematical modeling of geomechanical processes in rock mass have appeared. Currently, the researchers have accumulated ample knowledge on the efficiency of such problems in different problem solving. Unfortunately, the knowledge is disembodied, inexplicit and unformalized. At the same time, the use of such knowledge will allow researchers to greatly reduce labor content of mathematical modeling. Aimed at fixing this problem of current concern, this article describes an expert system for selecting a theory to be most suitable for development of an adequate mathematical model of geomechanical processes in rock mass with regard for the applied problem to be solved. The architecture of the expert system consists of the graphical user interface and two subsystems. The first subsystem of the intelligent database control is for editing, addition and elimination of production rules. The second subsystem realizes the inferential mechanism to administer progress of the problem solution on selection of a desired theory to construct an adequate mathematical model of geomechanical processes in rock mass.

keywords Expert system, artificial intelligence, production rule, mathematical model, rock mass, volume element, geomechanical processes
References

1. Kashyap S. K., Tanweer Md., Sinha A., Parhi D. R. An expert technique for optimization of underground mine support system. Computer Methods and Recent Advances in Geomechanics : Proceedings of the 14th International Conference of International Association for Computer Methods and Recent Advances in Geomechanics. 2015. pp. 561–565.
2. Bouff ard S. C. Benefi ts of process control systems in mineral processing grinding circuits. Mineral Engineering. 2015. Vol. 79. pp. 139–142. DOI: 10.1016/j.mineng.2015.06.006.
3. Devanand A., Kumar N. Prediction of CMRS rock mass rating using fuzzy logic. Proceedings of the 2nd International Conference on Advances in Computer Engineering and Applications. 2015. pp. 13–17. DOI: 10.1109/ICACEA.2015.7164685.
4. Zhang H., Li Q., Zhang L., Mi L. Early intelligent kick warning in well drilling based on fuzzy expert system. Journal of Southwest Petroleum University. 2016. Vol. 38. pp. 169–175. DOI: 10.11885/j.issn.1674-5086.2013.12.04.05.
5. Kumar A., Basu A. K., Sinsh J. P. Ground evaluation by expert system. Proceedings of the 12th International Conference on Computer Methods and Advances in Geomechanics. 2008. Vol. 3. pp. 1744–1749.
6. Kashyap S. K., Parhi D. R. K., Sinha A., Singh M. K., Singh B. K. Optimization of mine support parameters using Neural Network approach. Proceedings of the 12th International Conference on Computer Methods and Advances in Geomechanics. 2008. Vol. 3. pp. 1770–1779.
7. Nguyen V. U. Some fuzzy set applications in mining geomechanics. International Journal of Rock Mechanics and Mining Sciences. 1985. Vol. 22. pp. 369–379. DOI: 10.1016/0148-9062(85)90002-6.
8. Grady Booch, James Rumbaugh, Ivar Jacobson. Yazyk UML. Rukovodstvo polzovatelya (The Unified Modeling Language: Users Guide). Moscow : DMK Press, 2007. 496 p.
9. Liebowitz H. Razrushenie. Tom 2 (Fracture an Advanced Treatise. Volume 2). Moscow : Mir, 1975. 634 p.
10. Khalkechev R. K. Matematicheskaya model proverki materialov razlichnogo poryadka slozhnosti po trebovaniyu k opisaniyu ierarkhicheski-samopodobnoy sredoy (Mathematical model of materials check of various order of complexity on demand to the description the hierarchical and self-similar medium). Gornyy informatsionno-analiticheskiy byulleten = Mining Informational and Analytical Bulletin. 2015. No. 5. pp. 360–365.
11. Khalkechev R. K. Stokhasticheskiy metod opredeleniya elementarnykh obemov kristallicheskikh i kompozitsionnykh geomaterialov (Stochastic method of defi nition of voluentary units of crystalline and composite geomaterials). Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN = Bulletin of Kabardino-Balkarian Scientific Center of Russian Academy of Sciences. 2012. No. 2. pp. 38–41.
12. Joseph C. Giarratano, Gary D. Riley. Ekspertnye sistemy: printsipy razrabotki i programmirovanie (Expert Systems: Principles and Programming). Moscow : Williams, 2007. 1152 p.

Language of full-text russian
Full content Buy
Back