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Automatic generation of event sequence diagrams for guiding simulation based dynamic probabilistic risk assessment (SIMPRA) of complex systems

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  • Nejad, Hamed S.
  • Parhizkar, Tarannom
  • Mosleh, Ali

Abstract

Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate the risks associated with complex systems such as nuclear power plants, space missions, chemical plants, and military systems. A critical step in DPRA is generating risk scenarios which are used to enumerate and assess the probability of different outcomes. The classical approach to generating risk scenarios is not, however, sufficient to deal with the complexity of the above-mentioned systems.

Suggested Citation

  • Nejad, Hamed S. & Parhizkar, Tarannom & Mosleh, Ali, 2022. "Automatic generation of event sequence diagrams for guiding simulation based dynamic probabilistic risk assessment (SIMPRA) of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000886
    DOI: 10.1016/j.ress.2022.108416
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    1. Huang, Wencheng & Zhang, Yue & Yu, Yaocheng & Xu, Yifei & Xu, Minhao & Zhang, Rui & De Dieu, Gatesi Jean & Yin, Dezhi & Liu, Zhanru, 2021. "Historical data-driven risk assessment of railway dangerous goods transportation system: Comparisons between Entropy Weight Method and Scatter Degree Method," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
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    Cited by:

    1. Maidana, Renan G. & Parhizkar, Tarannom & Martin, Gabriel San & Utne, Ingrid B., 2024. "Dynamic probabilistic risk assessment with K-shortest-paths planning for generating discrete dynamic event trees," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Guo, Jian & Luo, Cheng & Ma, Kaijiang, 2023. "Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Hu, Yunpeng & Peng, Qibo & Ni, Qing & Wu, Xinfeng & Ye, Dongming, 2023. "Event-based safety and reliability analysis integration in model-based space mission design," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Li, Xinhong & Liu, Yabei & Zhang, Renren & Zhang, Nan, 2025. "Probabilistic failure assessment of oil and gas gathering pipelines using machine learning approach," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    5. Zhou, Siwei & Ye, Luyao & Xiong, Shengwu & Xiang, Jianwen, 2022. "Reliability analysis of dynamic fault trees with Priority-AND gates based on irrelevance coverage model," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Beal, John & Reihani, Seyed & Sakurahara, Tatsuya & Kee, Ernie & Mohaghegh, Zahra, 2025. "Modeling nuclear power plant piping reliability by coupling a human reliability analysis-based maintenance model with a physical degradation model," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    7. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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