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Risk Assessment of Rail Haulage Accidents in Inclined Tunnels with Bayesian Network and Bow-Tie Model


Affiliations
1 School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
 

Rail haulage system is an important part in mine production as people and materials are mainly transported through rail haulage equipment. The purpose of this communication is to establish a composite risk analysis model of rail haulage accidents in inclined tunnels based on Bayesian network and bow-tie model, which can be used to predict the risk of rail haulage accidents in mines and adopt relevant safety measures towards critical basic events. First, a simple case study of mapping fault tree into Bayesian network was introduced. Second, the risk level and critical basic events could be achieved according to forward analysis and backward analysis of Bayesian network with the help of GeNIe software. The obstacles on rails, unqualified rails and acceleration or deceleration were identified as the first category critical basic events of rail haulage accidents based on the above analysis. Third, acceleration or deceleration was chosen as the risk Bayesian node and a detailed analysis was made using bow-tie model. Twelve preventive safety measures were set on the left to prevent basic events and 10 mitigative safety measures were set on the right to mitigate accident consequences, the risk of rail haulage accidents in inclined tunnels can further be reduced by bow-tie analysis. Composite risk analysis model can be applied for similar risk analysis of rail haulage accidents.

Keywords

Bayesian Network, Bow-Tie Model, Rail Haulage Accidents, Risk Assessment.
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  • Chen, H. et al., Research on 10-year tendency of China coal mine accidents and the characteristics of human factors. Saf. Sci., 2012, 50, 745–750.

  • Cao, Q. G. et al., Risk management and workers safety behavior control in coal mine. Saf. Sci., 2012, 50, 903–913.

  • Yu, H. M. and Chen, H., Production output pressure and coal mine fatality seasonal variations in China, 2002–2011. J. Saf. Res., 2013, 47, 39–46.

  • Wei, C. F., Pei, Z. and Li, H. M., An induced OWA operator in coal mine safety evaluation. J. Comput. Syst. Sci., 2012, 78, 997–1005.

  • Chen, S. S., Xu, J. H. and Fan, Y., Evaluating the effect of coal mine safety supervision system policy in China’s coal mining industry: a two-phase analysis. Resour. Policy, 2015, 46, 12–21.

  • Liu, J. H. and Song, X. Y., Countermeasures of mine safety management based on behavior safety mode. Proc. Eng., 2014, 84, 144–150.

  • Wang, S. Y. and Zuo, H. Y., Safety diagnosis on coal mine production system based on fuzzy logic inference. J. Cent. South Univ., 2012, 19, 477–481.

  • Adenso-Diaz, B., Lev, B. and Artime, R., Simulation in dynamic environments: optimization of transportation inside a coal mine. IIE Trans., 2004, 36, 547–555.

  • Roumpos, C. et al., The optimal location of the distribution point of the belt conveyor system in continuous surface mining operations. Simul. Model. Pract. Theory, 2014, 47, 19–27.

  • Greberg, J. et al., Alternative process flow for underground mining operations: analysis of conceptual transport methods using discrete event simulation. Minerals, 2016, 65, 1–14.

  • Feng, X. L. et al., Wireless mobile monitoring system for tram rail transport in underground coal mine based on WMN. International Conference on Computational Aspects of Social Networks, 2010.

  • Shen, F. M., Chen, B. H. and Yang, J., Study on construction and quantification of evaluation index system of mine ventilation system. Proc. Earth Planet. Sci., 2009, 1, 114–122.

  • Xu, G. et al., Effective utilization of tracer gas in characterization of underground mine ventilation networks. Proc. Saf. Environ. Protect., 2016, 99, 1–10.

  • Jing, G. X., Feng, C. G. and Du, W., System safety analysis on rail haulage accident in inclined tunnel. China Saf. Sci. J., 2000, 3, 23–27 (in Chinese).

  • Niknejad, A. and Petrovic, D., A fuzzy dynamic Inoperability Input–output Model for strategic risk management in global production networks. Int. J. Product. Econ., 2016, 179, 44–58.

  • Liu, Y. L. et al., The assessment of traffic accident risk based on grey relational analysis and fuzzy comprehensive evaluation method. Nat. Hazards, 2017, 88, 1409–1422.

  • Li, Q. et al., Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion. Saf. Sci., 2012, 50, 1275–1283.

  • Chu, H. D. et al., Risk assessment of water inrush in karst tunnels based on two-class fuzzy comprehensive evaluation method. Arab. J. Geosci., 2017, 179, 1–12.

  • Li, H. et al., Risk Assessment of China’s overseas oil refining investment using a fuzzy-grey comprehensive evaluation method. Sustainability, 2017, 696, 1–18.

  • Zheng, G. Z. et al., Multihierarchical gray evaluation method to assess building energy conservation. J. Energy Eng., 2011, 2, 88–98.

  • Zhi, Y. L., Wang, H. M. and Liu, G., Study of fuzzy evaluation method on ecological civilization construction based on grey interval number. Int. J. U- and E-service, Sci. Technol., 2015, 2, 263–280.

  • Li, L., Liu, F. and Li, C. B., Customer satisfaction evaluation method for customized product development using entropy weight and analytic hierarchy process. Comput. Ind. Eng., 2014, 77, 80–87.

  • Chen, T. et al., A hybrid fuzzy evaluation method for safety assessment of food-waste feed based on entropy and the analytic hierarchy process methods. Exp. Syst. Appl., 2014, 41, 7328–7337.

  • Tan, X. D. et al., Evaluation of the effect of a health education campaign of HIV by using an analytical hierarchy process method. Int. J. Environ. Res. Public Health, 2007, 3, 254–259.

  • Sun, H. Y., Wang, S. F. and Hao, X. M., An improved analytic hierarchy process method for the evaluation of agricultural water management in irrigation districts of north China. Agric. Water Manag., 2017, 179, 324–337.

  • Liu, Y. J. et al., Study of a comprehensive assessment method for coal mine safety based on a hierarchical grey analysis. J. China Univ. Min. Technol., 2007, 1, 6–10.

  • Xia, C. Y. et al., Risk analysis and enhancement of cooperation yielded by the individual reputation in the spatial public goods game. IEEE Syst. J., 2017, 3, 1516–1525.

  • Wang, J. et al., Utility evaluation based on one-to-N mapping in the Prisoner’s dilemma game for interdependent networks. PLoS ONE, 2016, 12, 1–14.

  • Chen, M. H. et al., Evolution of cooperation in the spatial public goods game with adaptive reputation assortment. Phys. Lett. A, 2016, 380, 40–47.

  • Varouchakis, E. A., Palogos, I. and Karatzas, G. P., Application of Bayesian and cost benefit risk analysis in water resources management. J. Hydrol., 2016, 534, 390–396.

  • Khakzad, N., Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab. Eng. Syst. Saf., 2015, 138, 263–272.

  • Yu, R. J. et al., Crash risk analysis for Shanghai urban express-ways: a Bayesian semi-parametric modeling approach. Acci. Anal. Prevent., 2016, 95, 495–502.

  • Tang, C. H. et al., Risk analysis of emergent water pollution accidents based on a Bayesian Network. J. Environ. Manage., 2016, 165, 199–205.

  • Farmani, R., Henriksen, H. J. and Savic, D., An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination. Environ. Model. Software, 2009, 3, 303–310.

  • Hu, Y. et al., Software project risk analysis using Bayesian networks with causality constraints. Decis. Support Syst., 2013, 56, 439–449.

  • Weber, P. et al., Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell., 2012, 4, 671–682.

  • Landuyt, D. et al., A review of Bayesian belief networks in ecosystem service modelling. Environ. Model. Software, 2013, 46, 1–11.

  • Badreddine, A. and Amor, N. B., A Bayesian approach to construct bow tie diagrams for risk evaluation. Process Saf. Environ. Prot., 2013, 91, 159–171.

  • Khakzad, N., Khan, F. and Amyotte, P., Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. Prot., 2013, 91, 46–53.

  • Dianous, V. D. and Fiévez, C., A more explicit demonstration of risk control through the use of bow-tie diagrams and the evaluation of safety barrier performance. J. Hazard. Mater., 2006, 3, 220–233.

  • Thienen-Visser, K. V. et al., Bow-tie risk assessment combining causes and effects applied to gas oil storage in an abandoned salt cavern. Eng. Geol., 2014, 168, 149–166.

  • Aqlan, F. and Ali, E. M., Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry. J. Loss Prevent. Process Ind., 2014, 29, 39–48.

  • Mokhtari, K. et al., Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals. J. Hazard. Mater., 2011, 2, 465–475.

  • Yazdi, M., The Application of bow-tie method in hydrogen sulfide risk management using layer of protection analysis (LOPA). J. Failure Prevent., 2017, 2, 291–303.

  • Lu, L. L. et al., A comprehensive risk evaluation method for natural gas pipelines by combining a risk matrix with a bow-tie model. J. Nat. Gas Sci. Eng., 2015, 25, 124–133.

  • Anjuman, S., Rehan, S. and Tesfamariam, S., Risk analysis for oil and gas pipelines: a sustainability assessment approach using fuzzy based bow-tie analysis. J. Loss Prevent. Process Ind., 2012, 3, 505–523.

  • Chevreau, F. R., Wybo, J. L. and Cauchois, D., Organizing learning processes on risks by using the bow-tie representation. J. Hazard. Mater., 2006, 3, 276–283.

  • Targoutzidis, A., Incorporating human factors into a simplified ‘bow-tie’ approach for workplace risk assessment. Saf. Sci., 2010, 2, 145–156.

  • Ruijter, A. D. and Guldenmund, F., The bowtie method: a review. Saf. Sci., 2016, 88, 211–218.

  • Shan, X., Liu, K. and Sun, P. L., Risk analysis on leakage failure of natural gas pipelines by fuzzy Bayesian network with a bow-tie model. Sci. Program., 2017, 2017, 1–11.

  • Bilal, Z., Mohammed, K. and Brahim, H., Bayesian network and bow tie to analyse the risk of fire and explosion of pipelines. Process Saf. Prog., 2017, 2, 202–212.

  • Khakzad, N., Khan, F. and Amyotte, P., Quantitative risk analysis of offshore drilling operations: a Bayesian approach. Saf. Sci., 2013, 57, 108–117.

  • Yuan, Z. et al., Risk analysis of dust explosion scenarios using Bayesian networks. Risk Anal., 2015, 2, 278–291.

  • Yan, F. et al., Fuzzy Bayesian network-bow-tie analysis of gas leakage during biomass gasification. PLoS ONE, 2016, 7, 1–21.

  • Khakzad, N., Khan, F. and Amyotte, P., Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. Protect., 2013, 91, 46–53.

  • GeNie, Decision Systems Laboratory, University of Pittsburg.

  • Abimbola, M. et al., Safety and risk analysis of managed pressure drilling operation using Bayesian network. Saf. Sci., 2015, 76, 133–144.

  • State Administration of Work Safety, available at http://media.chinasafety.gov.cn:8090/iSystem/shigumain.jsp (accessed on 10 August 2017)

  • CCPS, Layers of protection analysis-simplified process risk assessment. New York: American Institute of Chemical Engineers, Center for Chemical Process Safety, 2001.


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  • Risk Assessment of Rail Haulage Accidents in Inclined Tunnels with Bayesian Network and Bow-Tie Model

Abstract Views: 339  |  PDF Views: 120

Authors

Qingwei Xu
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Kaili Xu
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

Abstract


Rail haulage system is an important part in mine production as people and materials are mainly transported through rail haulage equipment. The purpose of this communication is to establish a composite risk analysis model of rail haulage accidents in inclined tunnels based on Bayesian network and bow-tie model, which can be used to predict the risk of rail haulage accidents in mines and adopt relevant safety measures towards critical basic events. First, a simple case study of mapping fault tree into Bayesian network was introduced. Second, the risk level and critical basic events could be achieved according to forward analysis and backward analysis of Bayesian network with the help of GeNIe software. The obstacles on rails, unqualified rails and acceleration or deceleration were identified as the first category critical basic events of rail haulage accidents based on the above analysis. Third, acceleration or deceleration was chosen as the risk Bayesian node and a detailed analysis was made using bow-tie model. Twelve preventive safety measures were set on the left to prevent basic events and 10 mitigative safety measures were set on the right to mitigate accident consequences, the risk of rail haulage accidents in inclined tunnels can further be reduced by bow-tie analysis. Composite risk analysis model can be applied for similar risk analysis of rail haulage accidents.

Keywords


Bayesian Network, Bow-Tie Model, Rail Haulage Accidents, Risk Assessment.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi12%2F2530-2538