Abstract
Underground coal mining is one of the most hazardous activities in all around the word. Therefore, risk analysis has a remarkable role in the coal mining works. In this study, a new probabilistic approach is developed to evaluate the most important hazard of coal mining. For this aim, at first a fuzzy TOPSIS model is applied to rank the risks of the mining. By this way, it is possible to overcome the existing uncertainty of the risk ranking process. Application of the proposed procedure shows that the roof fall is the most important hazard in the Tabas Coal Mine in Iran. Afterward, this study tried to quantify the roof fall risk as the most important hazard in underground coal mining. Due to the related uncertainties associated with every mine, it is very difficult to predict the roof fall. As a result, development of a methodology for evaluation of roof fall risk under uncertainty condition has a key role in safety of underground coal mines. In this paper, a new approach for analyzing the risk of roof fall is presented. For this aim, the major factors influencing the stability of the roof are utilized in a Bayesian network-based model. The proposed method is illustrated with an application in Tabas Coal Mine. The results show that that BN-based model is a capable method for adjusting to uncertainties in the roof fall risk evaluation.
Similar content being viewed by others
References
H.S.B. Duzgun, H.H. Einstein, Assessment and management of roof fall risks in underground coal mines. Saf. Sci. 42(1), 23–41 (2004)
D.F. Cooper, The Australian and New Zealand standard on risk management, AS/NZS 4360: 2004, Tutor. Notes Broadleaf Cap. Int. Pty Ltd, pp. 128–151, 2004
N. Banaitienė, A. Banaitis, A. Norkus, Risk management in projects: peculiarities of Lithuanian construction companies. Int. J. Strateg. Prop. Manag. 15(1), 60–73 (2011)
A.T. Iannacchione, T.M. Brady, F. Varley, The application of major hazard risk assessment (MHRA) to eliminate multiple fatality occurrences in the US minerals industry. US Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Spokane Research Laboratory, 2008
M. Sarı, Risk assessment approach on underground coal mine safety analysis. Middle East Technical University. Department of Mining Engineering, 2002
A.V.Z. Durrheim, S.M. Roberts et al., Comparative seismology of the Witwatersrand Basin and Bushveld Complex and emerging technologies to manage the risk of rockbursting. J. South. Afr. Inst. Min. Metall. 105(6), 409–416 (2005)
S. Jian, W. Lian-guo, Z. Hua-lei, S. Yi-feng, Application of fuzzy neural network in predicting the risk of rock burst. Proc. Earth Planet. Sci. 1(1), 536–543 (2009)
R.L. Grayson, H. Kinilakodi, V. Kecojevic, Pilot sample risk analysis for underground coal mine fires and explosions using MSHA citation data. Saf. Sci. 47(10), 1371–1378 (2009)
J. Joy, Occupational safety risk management in Australian mining. Occup. Med. (Chic. III) 54(5), 311–315 (2004)
S.K. Palei, S.K. Das, Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Saf. Sci. 47(1), 88–96 (2009)
V.V. Khanzode, J. Maiti, P.K. Ray, A methodology for evaluation and monitoring of recurring hazards in underground coal mining. Saf. Sci. 49(8), 1172–1179 (2011)
Y. Jiang, H. Wang, S. Xue, Y. Zhao, J. Zhu, X. Pang, Assessment and mitigation of coal bump risk during extraction of an island longwall panel. Int. J. Coal Geol. 95, 20–33 (2012)
E. Ghasemi, M. Ataei, K. Shahriar, F. Sereshki, S.E. Jalali, A. Ramazanzadeh, Assessment of roof fall risk during retreat mining in room and pillar coal mines. Int. J. Rock Mech. Min. Sci. 54, 80–89 (2012)
A. Badri, S. Nadeau, A. Gbodossou, A new practical approach to risk management for underground mining project in Quebec. J. Loss Prev. Process Ind. 26(6), 1145–1158 (2013)
O.D. Eratak, Analysis and Modelling for Risk Management for Underground Coal Mines Safety (Middle East Technical University, Ankara, 2014)
C. Mark, M. Gauna, Evaluating the risk of coal bursts in underground coal mines. Int. J. Min. Sci. Technol. 26(1), 47–52 (2016)
S. Sadi Nezhad, K.K. Damghani, Application of a fuzzy TOPSIS method base on modified preference ratio and fuzzy distance measurement in assessment of traffic police centers performance. Appl. Soft. Comput. 10, 1028–1039 (2010)
E.K. Zavadskas, A. Zakarevicius, J. Antucheviciene, Evaluation of ranking accuracy in multi-criteria decisions. Informatica 17(4), 601–618 (2006)
L. Tupenaite, E.K. Zavadskas, A. Kaklauskas, Z. Turskis, M. Seniut, Multiple criteria assessment of alternatives for built and human environment renovation. J. Civ. Eng. Manag. 16(2), 257–266 (2010)
P. Liu, Z. Han et al., A fuzzy multi-attribute decision-making method under risk with unknown attribute weights. Technol. Econ. Dev. Econ. 17(2), 246–258 (2011)
J. Antuchevičienė, A. Zakarevičius, E.K. Zavadskas et al., Multiple criteria construction management decisions considering relations between criteria. Technol. Econ. Dev. Econ. 16(1), 109–125 (2010)
P. Liu et al., Multi-attribute decision-making method research based on interval vague set and TOPSIS method. Technol. Econ. Dev. Econ. 15(3), 453–463 (2009)
D. Kalibatas, E.K. Zavadskas, D. Kalibatiene, The concept of the ideal indoor environment in multi-attribute assessment of dwelling-houses. Arch. Civ. Mech. Eng. 11(1), 89–101 (2011)
C. Çetinkaya, E. Özceylan, M. Erbaş, M. Kabak, GIS-based fuzzy MCDA approach for siting refugee camp: a case study for southeastern Turkey. Int. J. Disaster Risk Reduct. 18, 218–231 (2016)
M. Gul, A.F. Guneri, A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J. Loss Prev. Process Ind. 40, 89–100 (2016)
X. Yu, S. Guo, J. Guo, X. Huang, Rank B2C e-commerce websites in e-alliance based on AHP and fuzzy TOPSIS. Expert Syst. Appl. 38(4), 3550–3557 (2011)
D. Heckerman, A. Mamdani, M.P. Wellman, Real-world applications of Bayesian networks. Commun. ACM 38(3), 24–26 (1995)
C.-F. Fan, Y.-C. Yu, BBN-based software project risk management. J. Syst. Softw. 73(2), 193–203 (2004)
F. Ülengin, Ş. Önsel, Y.I. Topçu, E. Aktaş, Ö. Kabak, An integrated transportation decision support system for transportation policy decisions: the case of Turkey. Transp. Res. Part A Policy Pract. 41(1), 80–97 (2007)
V. Khodakarami, N. Fenton, M. Neil, Project scheduling: improved approach to incorporate uncertainty using Bayesian networks. Proj. Manag. J. 38(2), 39 (2007)
K.-S. Chin, D.-W. Tang, J.-B. Yang, S.Y. Wong, H. Wang, Assessing new product development project risk by Bayesian network with a systematic probability generation methodology. Expert Syst. Appl. 36(6), 9879–9890 (2009)
T.-T. Chen, S.-S. Leu, Fall risk assessment of cantilever bridge projects using Bayesian network. Saf. Sci. 70, 161–171 (2014)
L. Zhang, X. Wu, M.J. Skibniewski, J. Zhong, Y. Lu, Bayesian-network-based safety risk analysis in construction projects. Reliab. Eng. Syst. Saf. 131, 29–39 (2014)
M. Abimbola, F. Khan, N. Khakzad, S. Butt, Safety and risk analysis of managed pressure drilling operation using Bayesian network. Saf. Sci. 76, 133–144 (2015)
B. Cai, Y. Liu, Q. Fan, A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels. Reliab. Eng. Syst. Saf. 150, 105–115 (2016)
J. Wu, R. Zhou, S. Xu, Z. Wu, Probabilistic analysis of natural gas pipeline network accident based on Bayesian network. J. Loss Prev. Process Ind. 46, 126–136 (2017)
X. Tian, Z. Xie, A. Wang, X. Yang, A new approach for Bayesian model averaging. Sci. China Earth Sci. 55(8), 1336–1344 (2012)
H. Zerrouki, A. Tamrabet, Safety and risk analysis of an operational heater using Bayesian network. J. Fail. Anal. Prev. 15(5), 657–661 (2015)
H. Zerrouki, H. Smadi, Bayesian belief network used in the chemical and process industry: a review and application. J. Fail. Anal. Prev. 17(1), 159–165 (2017)
A. Pillay, J. Wang, Modified failure mode and effects analysis using approximate reasoning. Reliab. Eng. Syst. Saf. 79(1), 69–85 (2003)
A. Gegov, Complexity management in fuzzy systems (Springer, Berlin, 2007)
L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
B. Chang, C.-W. Chang, C.-H. Wu, Fuzzy DEMATEL method for developing supplier selection criteria. Expert Syst. Appl. 38(3), 1850–1858 (2011)
C.-L. Hwang, K. Yoon, Multiple attribute decision making: methods and applications a state-of-the-art survey, vol. 186 (Springer, Berlin, 2012)
K. Abhishek, V.R. Kumar, S. Datta, S.S. Mahapatra, Application of JAYA algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA. Eng. Comput. 33, 1–19 (2016)
H.-J. Zimmermann, Fuzzy set theory and its applications (Springer, Berlin, 2011)
N. Jaramillo, M.L. Carreño, N. Lantada, Evaluation of social context integrated into the study of seismic risk for urban areas. Int. J. Disaster Risk Reduct. 17, 185–198 (2016)
G. Kabra, A. Ramesh, K. Arshinder, Identification and prioritization of coordination barriers in humanitarian supply chain management. Int. J. Disaster Risk Reduct. 13, 128–138 (2015)
M.Z. Naghadehi, R. Mikaeil, M. Ataei, The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran. Expert Syst. Appl. 36(4), 8218–8226 (2009)
S.K. Patil, R. Kant, A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Syst. Appl. 41(2), 679–693 (2014)
I. Macuzić, D. Tadić, A. Aleksić, M. Stefanović, A two step fuzzy model for the assessment and ranking of organizational resilience factors in the process industry. J. Loss Prev. Process Ind. 40, 122–130 (2016)
C.-T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114(1), 1–9 (2000)
R.L. Sousa, H.H. Einstein, Risk analysis during tunnel construction using bayesian networks: porto metro case study. Tunn. Undergr. Space Technol. 27(1), 86–100 (2012)
L. Uusitalo, Advantages and challenges of Bayesian networks in environmental modelling. Ecol. Modell. 203(3), 312–318 (2007)
G. Wang, T. Xu, T. Tang, T. Yuan, H. Wang, A Bayesian network model for prediction of weather-related failures in railway turnout systems. Expert Syst. Appl. 69, 247–256 (2017)
B. Yet, A. Constantinou, N. Fenton, M. Neil, E. Luedeling, K. Shepherd, A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study. Expert Syst. Appl. 60, 141–155 (2016)
S.J. Russell, P. Norvig, J.F. Canny, J.M. Malik, D.D. Edwards, Artificial intelligence: a modern approach, vol. 2 (Prentice Hall, Upper Saddle River, 2003)
T.D. Nielsen, F.V. Jensen, Bayesian networks and decision graphs (Springer, Berlin, 2009)
Anon, Basic design of Tabas Coal Mine Project. p. Vol 1 of 5, 2005
P. Guide, A guide to the project management body of knowledge, vol. 3 (Project Management Institute, Newtown Square, 2004)
Z.T. Bieniawski, Rock mechanics design in mining and tunnelling (Balkema, Boca Raton, 1984)
B. Das, Generating conditional probabilities for Bayesian networks: easing the knowledge acquisition problem. arXiv Prepr. cs/0411034, 2004
Acknowledgments
The authors are grateful to the Tabas Coal Mine management and engineers for their cooperation in conducting this study. The authors would also like to thank all the mining experts who took part in our research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Javadi, M., Saeedi, G. & Shahriar, K. Developing a New Probabilistic Approach for Risk Analysis, Application in Underground Coal Mining. J Fail. Anal. and Preven. 17, 989–1010 (2017). https://doi.org/10.1007/s11668-017-0325-0
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11668-017-0325-0