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Probabilistic risk assessment on maritime spent nuclear fuel transportation (Part II: Ship collision probability)

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  • Christian, Robby
  • Kang, Hyun Gook

Abstract

This paper proposes a methodology to assess and reduce risks of maritime spent nuclear fuel transportation with a probabilistic approach. Event trees detailing the progression of collisions leading to transport casks’ damage were constructed. Parallel and crossing collision probabilities were formulated based on the Poisson distribution. Automatic Identification System (AIS) data were processed with the Hough Transform algorithm to estimate possible intersections between the shipment route and the marine traffic. Monte Carlo simulations were done to compute collision probabilities and impact energies at each intersection. Possible safety improvement measures through a proper selection of operational transport parameters were investigated. These parameters include shipment routes, ship's cruise velocity, number of transport casks carried in a shipment, the casks’ stowage configuration and loading order on board the ship.

Suggested Citation

  • Christian, Robby & Kang, Hyun Gook, 2017. "Probabilistic risk assessment on maritime spent nuclear fuel transportation (Part II: Ship collision probability)," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 136-149.
    • Handle: RePEc:eee:reensy:v:164:y:2017:i:c:p:136-149
    DOI: 10.1016/j.ress.2016.11.017
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    References listed on IDEAS

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    1. Goerlandt, Floris & Kujala, Pentti, 2011. "Traffic simulation based ship collision probability modeling," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 91-107.
    2. Montewka, Jakub & Hinz, Tomasz & Kujala, Pentti & Matusiak, Jerzy, 2010. "Probability modelling of vessel collisions," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 573-589.
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    Cited by:

    1. Zhang, Mingyang & Montewka, Jakub & Manderbacka, Teemu & Kujala, Pentti & Hirdaris, Spyros, 2021. "A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Liu, Qiang & Tang, Aiping & Huang, Delong & Huang, Ziyuan & Zhang, Bin & Xu, Xiuchen, 2022. "Total probabilistic measure for the potential risk of regional roads exposed to landslides," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Gino J. Lim & Jaeyoung Cho & Selim Bora & Taofeek Biobaku & Hamid Parsaei, 2018. "Models and computational algorithms for maritime risk analysis: a review," Annals of Operations Research, Springer, vol. 271(2), pages 765-786, December.
    5. Tao, Longlong & Chen, Liwei & Ge, Daochuan & Yao, Yuantao & Ruan, Fang & Wu, Jie & Yu, Jie, 2022. "An integrated probabilistic risk assessment methodology for maritime transportation of spent nuclear fuel based on event tree and hydrodynamic model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    6. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    7. Tao, Longlong & Wu, Jie & Ge, Daochuan & Chen, Liwei & Sun, Ming, 2022. "Risk-informed based comprehensive path-planning method for radioactive materials road transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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