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DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation

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  • Laobing Zhang
  • Gabriele Landucci
  • Genserik Reniers
  • Nima Khakzad
  • Jianfeng Zhou

Abstract

Historical data analysis shows that escalation accidents, so‐called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent‐based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent‐based modeling technique explains the domino effects from a bottom‐up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher‐level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large‐scale complicated cases.

Suggested Citation

  • Laobing Zhang & Gabriele Landucci & Genserik Reniers & Nima Khakzad & Jianfeng Zhou, 2018. "DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1585-1600, August.
    • Handle: RePEc:wly:riskan:v:38:y:2018:i:8:p:1585-1600
    DOI: 10.1111/risa.12955
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    References listed on IDEAS

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    1. Nima Khakzad & Faisal Khan & Paul Amyotte & Valerio Cozzani, 2013. "Domino Effect Analysis Using Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 33(2), pages 292-306, February.
    2. Landucci, Gabriele & Argenti, Francesca & Tugnoli, Alessandro & Cozzani, Valerio, 2015. "Quantitative assessment of safety barrier performance in the prevention of domino scenarios triggered by fire," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 30-43.
    3. Necci, Amos & Cozzani, Valerio & Spadoni, Gigliola & Khan, Faisal, 2015. "Assessment of domino effect: State of the art and research Needs," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 3-18.
    4. Xuwei Chen & John Meaker & F. Zhan, 2006. "Agent-Based Modeling and Analysis of Hurricane Evacuation Procedures for the Florida Keys," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 38(3), pages 321-338, July.
    5. Khakzad, Nima & Reniers, Genserik & Abbassi, Rouzbeh & Khan, Faisal, 2016. "Vulnerability analysis of process plants subject to domino effects," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 127-136.
    6. Khakzad, Nima & Reniers, Genserik, 2015. "Using graph theory to analyze the vulnerability of process plants in the context of cascading effects," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 63-73.
    7. Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
    8. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
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    Cited by:

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    3. Chen, Chao & Reniers, Genserik & Khakzad, Nima, 2021. "A dynamic multi-agent approach for modeling the evolution of multi-hazard accident scenarios in chemical plants," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Hou, Lei & Wu, Xingguang & Wu, Zhuang & Wu, Shouzhi, 2020. "Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. Ding, Long & Khan, Faisal & Ji, Jie, 2022. "A novel vulnerability model considering synergistic effect of fire and overpressure in chemical processing facilities," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Ding, Long & Khan, Faisal & Ji, Jie, 2020. "A novel approach for domino effects modeling and risk analysis based on synergistic effect and accident evidence," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. Ding, Long & Khan, Faisal & Abbassi, Rouzbeh & Ji, Jie, 2019. "FSEM: An approach to model contribution of synergistic effect of fires for domino effects," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 271-278.

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