Developing a safety risk model for railway network considering tunnel fall using Bayesian network

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Civil Engineering, Payame Noor University, Department of Center of North Tehran

2 professor

Abstract

Evaluating the safety of the railway network as the fundamental part of the economy development reflects the safety of the entire transportation network of a country. One of the challenges threatening the railway safety is the railway tunnel conditions in terms of safe operating during operation period and analyzing the consequences of its hazards. Today, Bayesian network are the most identified methods to graphically model the causes of accidents and their conditional probabilities. In order to develop a model that best matches the real condition of the railway tunnels risks, modeling process divided in to two sections; in the first section, the compromised causes that identified and concluded by the specialists scored according to their frequency of happening and the related network constructed. Second section joints the cause and consequence analysis of different scenarios offering remedial actions and concluding the final model based on severity degrees defined. Outcome of the model shows the high probability for water penetration in tunnels without permanent lining as the most important cause and the first degree of the accident severity according to the guideline as the most frequent consequence of the risks identified.

Keywords

References

Beard, A. N. 2010. “Tunnel safety, risk assessment and decision-making”. Tunn. Undergr. Sp. Tech., 25(1): 91-94.
Chung, H., Lee, I. M., Jung, J. H. and Park, J. 2019. “Bayesian networks-based shield TBM risk management system: Methodology development and application”. KSCE J. Civ. Eng., 23(1): 452-465.
Gui, S. and Gui, R. 2019. “Auto-extraction of stratified interface in the underground space-based on Bayesian detection algorithm with statistical fitting of probability density by actual data”. Sustain. Cities Soc., 101430.
Jena, J. K., Verma, A. K., Kumar, U. and Ajit, S. 2019. “Tunnel QRA: Present and future perspectives”. In: Kapur, P., Klochkov, Y., Verma, A. and Singh, G. (Eds.) System Performance and Management Analytics. Asset Analytics (Performance and Safety Management), Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_31
Lyu, H. M., Shen, S. L., Zhou, A. and Yang, J. 2019. “Perspectives for flood risk assessment and management for mega-city metro system”. Tunn. Undergr. Sp. Tech. 84: 31-44.
Otto, A., Kellermann, P., Thieken, A. H., Manez Costa, M., Carmona, M. and Bubeck P.  2019. “Risk reduction partnerships in railway transport infrastructure in an Alpine environment”. Int. J. Disaster Risk Reduction, 33: 385-397.
Rakoczy, A. M., Wilk, S. T. and Jones, M. C. 2019. “Security and safety of rail transit tunnels”. Transport. Res. Record, https://doi.org/10.1177/0361198118822819
Ronchi, E., Colonna, P., Capote, J., Alvear, D., Berloco, N. and Cuesta, A. 2012. “The evaluation of different evacuation models for assessing road tunnel safety analysis”. Tunn. Undergr. Sp. Tech., 30: 74-84.
Su, M., Wang, P., Xue, Y., Qiu, D., Li, Z., Xia, T. and Li, G. 2019. “Prediction of risk in submarine tunnel construction by multi-factor analysis: A collapse prediction model”. Marine Georesour. Geotech., 37(9): 1119-1129.
Wang, Q., Luan, Y., Jiang, B., Li, S., He, M., Sun, H., Qin, Q. and Lu, W. 2019. “Study on key technology of tunnel fabricated arch and its mechanical mechanism in the mechanized construction”. Tunn. Undergr. Sp. Tech., 83: 187-194.
Zhou, C., Luo, H., Fang, W., Wei, R. and Ding, L. 2019. “Cyber-physical-system-based safety monitoring for blind hoisting with the internet of things: A case study”. Automat. Constr., 97: 138-150.
 

Files

History

  • Receive Date: 04 October 2019
  • Revise Date: 06 January 2021
  • Accept Date: 07 February 2021

Share

How to cite

Statistics