Skip to main content
Log in

Prediction of global stability in room and pillar coal mines

Natural Hazards Aims and scope Submit manuscript

Abstract

Global stability is a necessary prerequisite for safe retreat mining and one of the crucial and complex problems in room and pillar mining, so its prediction plays an important role in the safety of retreat mining and the reduction of pillar failure risk. In this study, we have tried to develop predictive models for anticipating global stability. For this purpose, two of the most popular techniques, logistic regression analysis and fuzzy logic, were taken into account and a predictive model was constructed based on each. For training and testing of these models, a database including 80 retreat mining case histories from 18 room and pillar coal mines, located in West Virginia State, USA, was used. The models predict global stability based on the major contributing parameters of pillar stability. It was found that both models can be used to predict the global stability, but the comparison of two models, in terms of statistical performance indices, shows that the fuzzy logic model provides better results than the logistic regression. These models can be applied to identify the susceptibility of pillar failure in panels of coal mines, and this may help to reduce the casualties resulting from pillar instability. Finally, the sensitivity analysis was performed on database to determine the most important parameters on global stability. The results revealed that the pillar width is the most important parameter, whereas the depth of cover is the least important one.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  • Acaroglu O (2011) Prediction of thrust and torque requirements of TBMs with fuzzy logic models. Tunn Undergr Space Technol 26:267–275

    Article  Google Scholar 

  • Alvarez Grima M (2000) Neuro-fuzzy modeling in engineering geology. Balkema, Rotterdam

    Google Scholar 

  • Alvarez Grima M, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349

    Article  Google Scholar 

  • Aydin A (2004) Fuzzy set approaches to classification of rock masses. Eng Geol 74:227–245

    Article  Google Scholar 

  • Azadeh A, Osanloo M, Ataei M (2010) A new approach to mining method selection based on modifying the Nicholas technique. Appl Soft Comput 10:1040–1061

    Article  Google Scholar 

  • Dodagoudar GR, Venkatachalam G (2000) Reliability analysis of slopes using fuzzy sets theory. Comput Geotech 27:101–115

    Article  Google Scholar 

  • Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51:241–256

    Article  Google Scholar 

  • Feddock JE and Ma J (2006) Safety: a review and evaluation of current retreat mining practice in Kentucky. In: Proceedings of the 25th international conference on ground control in mining. West Virginia University, Morgantown, USA, pp 366–373

  • Ghasemi E, Shahriar K (2012) A new coal pillars design method in order to enhance safety of retreat mining in room and pillar mines. Saf Sci 50:579–585

    Article  Google Scholar 

  • Ghasemi E, Shahriar K, Sharifzadeh M, Hashemolhosseini H (2010) Quantifying the uncertainty of pillar safety factor by Monte Carlo simulation—a case study. Arch Min Sci 55:623–635

    Google Scholar 

  • Ghasemi E, Amini H, Ataei M, Khalokakaei R (2012) Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci. doi:10.1007/s12517-012-0703-6

    Google Scholar 

  • Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. Eng Geol 66:39–51

    Article  Google Scholar 

  • Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17:61–72

    Article  Google Scholar 

  • Hartman HL (1987) Introductory mining engineering. Wiley, New York

    Google Scholar 

  • Heasley KA (1997) A new laminated overburden model for coal mine design. In: Proceedings of the new technology for ground control in retreat mining. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9446, pp 60–73

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York

    Book  Google Scholar 

  • Hustrulid WA (1982) Underground mining methods handbook. SME-AIME, New York

    Google Scholar 

  • Iphar M, Goktan RM (2006) An application of fuzzy sets to the diggability index rating method for surface mine equipment selection. Int J Rock Mech Min Sci 43:253–266

    Article  Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Jong YH, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41:533–538

    Article  Google Scholar 

  • Kleinbaum DG, Klein M (2002) Logistic regression: a self learning text. Springer, New York

    Google Scholar 

  • Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34:223–232

    Article  Google Scholar 

  • Leech NL, Barrett KC, Morgan GA (2005) SPSS for intermediate statistics: use and interpretation. Lawrence Erlbaum, New Jersey

    Google Scholar 

  • Maiti J, Bhattacherjee A (1999) Evaluation of risk of occupational injuries among underground coal mine workers through multinomial logit analysis. J Saf Res 30:93–101

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  Google Scholar 

  • Mark C (2006) The evolution of intelligent coal pillar design: 1981–2006. Proceedings of the 25th international conference on ground control in mining. West Virginia University, Morgantown, pp 325–334

    Google Scholar 

  • Mark C (2010) Pillar design for deep cover retreat mining: ARMPS version 6 (2010). In: Proceedings of the third international workshop on coal pillar mechanics and design. West Virginia University, Morgantown, pp 106–121

  • Mark C and Chase F (1997) Analysis of Retreat Mining Pillar Stability (ARMPS). In: Proceedings of the new technology for ground control in retreat mining. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9446, pp 17–34

  • Mark C, Chase F, Pappas DM (2003) Reducing the risk of ground falls during pillar recovery. Trans Soc Min Eng 314:153–160

    Google Scholar 

  • MATLAB 7.6 (2008) Software for technical computing and model based design. The Math Works, Cambridge

  • Molinda GM and Mark C (1994) The coal mine roof rating (CMRR)—a practical rock mass classification for coal mines. US Department of the Interior, Bureau of Mines, Information Circular 9387, 83 pp

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171–191

    Article  Google Scholar 

  • Nguyen VU and Ashworth E (1985) Rock mass classification by fuzzy sets. In: Proceedings of the 26th US symposium on rock mechanics, Rapid City, pp 937–945

  • Palei SK, Das SK (2009) Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Saf Sci 47:88–96

    Article  Google Scholar 

  • Park HJ, Um J, Woo I, Kim JW (2012) Application of fuzzy set theory to evaluate the probability of failure in rock slope. Eng Geol 125:92–101

    Article  Google Scholar 

  • Peng SS (2008) Coal mine ground control. Department of Mining Engineering, College of Engineering and Mineral Resources, West Virginia University, Morgantown

    Google Scholar 

  • Recio-Gordo D, Jimenez R (2012) A probabilistic extension to the empirical ALPS and ARMPS systems for coal pillar design. Int J Rock Mech Min Sci 52:181–187

    Article  Google Scholar 

  • Rezaei M, Monjezi M, Varjani AY (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49:298–305

    Article  Google Scholar 

  • Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York

    Google Scholar 

  • Saboya F Jr, Alves MD, Pinto WD (2006) Assessment of failure susceptibility of soil slops using fuzzy logic. Eng Geol 86:211–224

    Article  Google Scholar 

  • SPSS 16.0 (2007) Statistical analysis software (Standard Version). SPSS, New York

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  Google Scholar 

  • Wattimena RK, Kramadibrata S, Sidi ID, Azizi MA (2013) Developing coal pillar stability chart using logistic regression. Int J Rock Mech Min Sci 58:55–60

    Google Scholar 

  • World Coal Association (2013) Coal facts 2013. http://www.worldcoal.org/bin/pdf/original_pdf_file/coal_facts_2013(11_09_2013).pdf. Accessed 11 September 2013

  • Zadeh LA (1965) Fuzzy sets. Inf control 8:338–353

    Google Scholar 

  • Zhou J, Li X, Shi X, Wei W, Wu B (2011) Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods. Trans Nonferrous Met Soc China 21:2734–2743

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank all the people who helped in the preparation of the paper, especially Dr. Christopher Mark and Mrs. Ifa Mahboobi. The authors are also very grateful to the anonymous reviewer for his/her useful comments and constructive suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ebrahim Ghasemi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ghasemi, E., Ataei, M. & Shahriar, K. Prediction of global stability in room and pillar coal mines. Nat Hazards 72, 405–422 (2014). https://doi.org/10.1007/s11069-013-1014-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-013-1014-2

Keywords

Navigation