Skip to main content
Log in

Application of fuzzy logic for predicting roof fall rate in coal mines

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Roof fall is one of the serious hazards associated with underground coal mining. Roof fall can cause fatal and non-fatal injuries on miners, stoppages in mining operations and equipment breakdowns. Therefore, accurate prediction of roof fall rate is very important in controlling and eliminating of related problems. In this study, the fuzzy logic was applied to predict roof fall rate in coal mines. The predictive fuzzy model was implemented on fuzzy logic toolbox of MATLAB® using Mamdani algorithm and was developed based on experts’ knowledge and also a database including 109 datasets of roof performance from US coal mines. 22 datasets of this database were used to assess the performance of this fuzzy model. The comparison between obtained results from model and actual roof fall rate showed that the fuzzy model can predict roof fall rate very well.

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
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. MSHA (2005) Quarterly employment and production: accidents/injuries/illnesses reported to MSHA under 30 CFR Part 50. US Department of Labor, Mine Safety and health Administration, Office of Injury and Employment Information

  2. Molinda GM, Mark C, Dolinar D (2000) Assessing coal mine roof stability through roof fall analysis. In: Proceedings of the new technology for coal mine roof support. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9453, pp 53–72

  3. van der Merwe JN, van Vuuren JJ, Butcher R, Canbulat I (2001) Causes of falls of roof in South African collieries. Safety in Mines Research Advisory committee (SIMRAC). Final Project Report, Report No. COL613

  4. Molinda GM (2003) Geologic hazards and roof stability in coal mines. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, NIOSH Publication No. 9466

  5. Deb D (2003) Analysis of coal mine roof fall rate using fuzzy reasoning techniques. Int J Rock Mech Min Sci 40:251–257

    Article  Google Scholar 

  6. Duzgun HSB, Einstein HH (2004) Assessment and management of roof fall risks in underground coal mines. Saf Sci 42:23–41

    Article  Google Scholar 

  7. Duzgun HSB (2005) Analysis of roof fall hazards and risk assessment for Zanguldak coal basin underground mines. Int J Coal Geol 64:104–115

    Article  Google Scholar 

  8. Palei SK, Das SK (2008) Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines. Int J Coal Geol 75:241–247

    Article  Google Scholar 

  9. Shahriar K, Bakhtavar E (2009) Geotechnical risks in underground coal mines. J Appl Sci 9:2137–2143

    Article  Google Scholar 

  10. 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 

  11. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

  12. Cox EA (1992) Fuzzy fundamentals. IEEE Spectr 29:58–61

    Article  Google Scholar 

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

    MATH  Google Scholar 

  14. Jang JR (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  15. Azimi Y, Osanloo M, Aakbarpour-Shirazi M, Aghajani Bazzazi A (2010) Prediction of the blastability designation of rock masses using fuzzy sets. Int J Rock Mech Min Sci 47:1126–1140

    Article  Google Scholar 

  16. Grima MA, Bruines PA, Verhoef PNW (2000) Modelling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Space Technol 15:259–269

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  18. 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 

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

  20. Habibagahi G, Katebi S (1996) Rock mass classification using fuzzy sets. Iran J Sci Technol 20(3):273–284

    Google Scholar 

  21. Sonmez H, Gokceoglu C, Ulusay R (2003) An application of fuzzy sets to the geological strength index (GSI) system used in rock engineering. Eng Appl Artif Intell 16(3):251–269

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Jiang YM, Park DW, Deb D, Sanford R (1997) Application of fuzzy set theory in the evaluation of roof categories in longwall mining. Min Eng 49(3):53–57

    Google Scholar 

  24. Bascetin A, Oztas O, Kanli AI (2006) EQS: a computer software using fuzzy logic for equipment selection in mining engineering. J S Afr Inst Min Metall 106:63–70

    Google Scholar 

  25. Karadogan A, Kahriman A, Ozer U (2008) Application of fuzzy set theory in the selection of underground mining method. J S Afr Inst Min Metall 108:73–79

    Google Scholar 

  26. Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Space Technol 23:600–608

    Article  Google Scholar 

  27. Khademi Hamidi J, Shahriar K, Rezai B, Bejari H (2010) Application of fuzzy set theory to rock engineering classification systems: an illustration of the rock mass excavability index. Rock Mech Rock Eng 43:335–350

    Article  Google Scholar 

  28. 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 

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

    Article  Google Scholar 

  30. Tzamos S, Sofianos AI (2006) Extending the Q system’s prediction of support in tunnels employing fuzzy logic and extra parameters. Int J Rock Mech Min Sci 43:938–949

    Article  Google Scholar 

  31. Fisne A, Kuzu C, Hudaverdi T (2010) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess. doi:10.1007/s10661-010-1470-z

    Google Scholar 

  32. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37:2637–2643

    Article  Google Scholar 

  33. 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 

  34. Li W, Mei S, Zai S, Zhao S, Liang X (2006) Fuzzy models for analysis of rock mass displacements due to underground mining in mountainous areas. Int J Rock Mech Min Sci 43:503–511

    Article  Google Scholar 

  35. Li W, Liu L, Dai L (2010) Fuzzy probability measures (FPM) based non-symmetric membership function: Engineering examples of ground subsidence due to underground mining. Eng Appl Artif Intell 23:420–431

    Article  Google Scholar 

  36. 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 

  37. Ataei M, Khalokakaei R, Hossieni M (2009) Determination of coal mine mechanization using fuzzy logic. Min Sci Technol 19:149–154

    Google Scholar 

  38. 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 

  39. Kayabasi A, Gokceoglu C, Ercanoglu E (2003) Estimating the deformation modulus of rock masses: a comparative study. Int J Rock Mech Min Sci 40:55–63

    Article  Google Scholar 

  40. 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 

  41. Sonmez H, Tuncay E, Gokceoglu C (2004) Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara Agglomerate. Int J Rock Mech Min Sci 41:717–729

    Article  Google Scholar 

  42. Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst Appl 37:2265–2272

    Article  Google Scholar 

  43. Molinda GM, Mark C (1994) The coal mine roof rating (CMRR)—a practical rock mass classification for coal mines. US Bureau of Mines (USBM), IC 9387, 83 pp

  44. Molinda GM, Mark C, Bauer ER, Babich DR, Pappas DM (1998) Factors influencing intersection stability in US coal mines. In: Proceedings of the 17th international conference on ground control in mining, Morgantown. West Virginia University, USA, pp 267–275

  45. MATLAB® 7.6 (2008) Software for technical computing and model-based design. The Math Works Inc

Download references

Acknowledgments

The authors would like to thank Mrs. Ifa Mahboobi for her kind help during the preparation of manuscript and the anonymous reviewers for their valuable and constructive comments.

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. Application of fuzzy logic for predicting roof fall rate in coal mines. Neural Comput & Applic 22 (Suppl 1), 311–321 (2013). https://doi.org/10.1007/s00521-012-0819-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0819-3

Keywords

Navigation