Illumination of Contributing Parameters of Uneven Break in Narrow Vein Mine

  • Hyongdoo JangEmail author
  • Sina Taheri
  • Erkan Topal
  • Youhei Kawamura
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)


One of the principal challenge facing the stope production in underground mining is the overbreak and underbreak (UB: uneven break). Although the UB features a critical economic fallout to the entire mining process, it is much inevitable and usually left as an unpredictable phenomenon in underground mines. The complex mechanism of UB must be examined to minimize the UB phenomenon. In this study, the contribution of ten primary UB causative parameters is scrutinized investigating a published UB prediction ANN model. The inputs (UB causative factors) contributions to the output (percentage of UB) of the ANN model were analyzed using Profile methodology (PM). The results PM revealed the essential importance of geological parameters to UB phenomenon as the calculated contributions of adjusted Q-rate (GAQ) and average horizontal to vertical stress ratio (GSK) are 20.48% and 18.12% respectively. Also, the trends of the other eight UB causative factors were investigated. The findings of this study can be used as a reference in stope design and reconciliation processes to maximize the productivity of the underground mine.


Overbreak Underbreak Narrow vein ANN 


  1. Barton, N.R., Lien, R., Lunde, J.: Engineering classification of rock masses for the design of tunnel support. Rock Mech. 6, 189–236 (1974). Southern Nevada: Geological Society of America Bulletin 88, 943–959CrossRefGoogle Scholar
  2. 2.
    Bieniawski, Z.T.: Engineering classification of jointed rock masses. Civ. Eng. South Afr. 15(12), 335–343 (1973)Google Scholar
  3. Bieniawski, Z.T.: Geomechanics classification of rock masses and its application in tunnelling. Paper presented at the Advances in Rock Mechanics, Proceedings of the 3rd International Society of Rock Mechanics Congress, Denver, Colorado (1974)Google Scholar
  4. Dimopoulos, Y., Bourret, P., Lek, S.: Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Process. Lett. 2(6), 1–4 (1995)CrossRefGoogle Scholar
  5. Garson, G.D.: Interpreting neural-network connection weights. AI Expert 6(4), 46–51 (1991)Google Scholar
  6. Germain, P., Hadjigeorgiou, J.: Influence of stope geometry and blasting patterns on recorded overbreak. Int. J. Rock Mech. Min. Sci. 34(3), 115.e111–115.e112 (1997). Scholar
  7. Jang, H.: Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network. Ph.D., Curtin University, Kalgoorlie, WA, Australia (2014)Google Scholar
  8. Jang, H., Topal, E., Kawamura, Y.: Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system. Appl. Soft Comput. 32, 1–12 (2015a)CrossRefGoogle Scholar
  9. Jang, H., Topal, E., Kawamura, Y.: Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses. J. South Afr. Inst. Min. Metall. 115, 449–456 (2015b)CrossRefGoogle Scholar
  10. Jang, H., Topal, E., Kawamura, Y.: Illumination of parameter contributions on uneven break phenomenon in underground stoping mines. Int. J. Min. Sci. Technol. 26(6), 1095–1100 (2016)CrossRefGoogle Scholar
  11. Jang, H.D.: Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network. Ph.D. Dissertation, Western Australian School of Mines, Curtin University, Kalgoorlie, Western Australia, Australia (2014)Google Scholar
  12. Lang, B.D.A.: Span design for entry-type excavations. Master, University of British Columbia, Vancouver, Canada (1994)Google Scholar
  13. Lek, S., Belaud, A., Dimopoulos, I., Lauga, J., Moreau, J.: Improved estimation, using neural networks, of the food consumption of fish populations. Mar. Freshw. Res. 46(8), 1229–1236 (1995)CrossRefGoogle Scholar
  14. Levenberg, K.: A method for the solution of certain problems in least squares. Q. Appl. Math. 2, 164–168 (1944). doi:citeulike-article-id:1951284CrossRefGoogle Scholar
  15. Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)CrossRefGoogle Scholar
  16. Mathews, K.E., Hoek, E., Wyllie, D.C., Stewart, S.: Prediction of stable excavation spans for mining at depths below 1000 m in hard rock. In: Paper presented at the CANMET DSS Serial No: 0sQ80-00081, Ottawa (1981)Google Scholar
  17. Nickson, S.D.: Cable Support Guidelines for Underground Hard Rock Mine Operations. University of British Columbia, Vancouver, Canada (1992)Google Scholar
  18. Olden, J.D., Jackson, D.A.: Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154(1), 135–150 (2002)CrossRefGoogle Scholar
  19. Pakalnis, R.C.T.: Empirical stope design at Ruttan mine. In: Paper presented at the Department of Mining and Minerals Processing, Vancouver (1986)Google Scholar
  20. Pakalnis, R.C.T., Hughes, P.B.: Sublevel Stoping. In: Darling, P. (ed.) SME Mining Engineering Handbook, 3rd edn. Society for Mining, Metallurgy, and Exploration, Inc., United States of America (2011)Google Scholar
  21. Potvin, Y.: Empirical open stope design in Canada. Ph.D., University of British Columbia, Vancouver, Canada (1988)Google Scholar
  22. Stewart, P., Slade, J., Trueman, R.: The effect of stress damage on dilution in narrow vein mines. In: Paper presented at the 9th AusIMM Underground Operators Conference 2005 (2005)Google Scholar
  23. Stiehr, J.F., Dean, J.: ISEE blasters’ handbook, pp. 442–452. International Society of Explosives, Cleveland (2011)Google Scholar
  24. Yang, Y.-J., Zhang, Q.: The application of neural networks to rock engineering systems (RES). Int. J. Rock Mech. Min. Sci. 35(6), 727–745 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Western Australian School of MinesCurtin UniversityKalgoorlieAustralia
  2. 2.Graduate School of International Resource Sciences, Department of Earth Resource Engineering and Environmental ScienceAkita UniversityAkitaJapan

Personalised recommendations