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An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks

Author

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  • An, Xu
  • Yin, Zhiming
  • Tong, Qi
  • Fang, Yiping
  • Yang, Ming
  • Yang, Qiaoqiao
  • Meng, Huixing

Abstract

The interactions of external disruptions and technical-human-organizational factors in emergency operations are usually observed. Resilience assessment of emergency systems can improve emergency response capability and system functional recovery. The increasing complexity and coupling of factors in emergency response systems need to be investigated from a system resilience perspective. In this paper, we propose to integrate a multi-stage System-Theoretic Accident Model and Processes (STAMP) with a dynamic Bayesian network (DBN) for the resilience assessment of emergency response systems. In the proposed methodology, emergency response systems are viewed as multi-step emergency operations for STAMP to analyze the hierarchical control and feedback structures. The output of multi-stage STAMP in controllers, actuators, sensors, and controlled processes is applied to develop a DBN for resilience assessment. For known external shocks (e.g., natural disasters), the effects of external shocks on the system are decomposed into subsystems or components. System degradation and recovery models are established. Regarding unknown external disruption (e.g., unforeseen failure modes), degeneration and recovery are temporally integrated into the analysis of system functionality. System performance is evaluated through the combination of socio-technical factors and external disasters. Eventually, the resilience of emergency response systems is obtained from the performance curves. The results demonstrate that the proposed model can evaluate system resilience after the system suffers from external disasters.

Suggested Citation

  • An, Xu & Yin, Zhiming & Tong, Qi & Fang, Yiping & Yang, Ming & Yang, Qiaoqiao & Meng, Huixing, 2023. "An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003599
    DOI: 10.1016/j.ress.2023.109445
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    Citations

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    Cited by:

    1. Song, Xueying & Qi, Lei & Liu, Shiyan & Ding, Shuiting & Li, Daqing, 2024. "Simple analysis of complex system safety based on Finite State Machine Network and phase space theory," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    2. Tan, Xinxin & Xiao, Shenbin & Yang, Yu & Khakzad, Nima & Reniers, Genserik & Chen, Chao, 2024. "An agent-based resilience model of oil tank farms exposed to earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Qiu, Na & Liu, Xiuquan & Li, Yanwei & Hu, Pengji & Chang, Yuanjiang & Chen, Guoming & Meng, Huixing, 2024. "Dynamic catastrophe analysis of deepwater mooring platform/riser/wellhead coupled system under ISW," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    4. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Chen, Jiayu. & Yao, Boqing & Lu, Qinhua & Wang, Xuhang & Yu, Pingchao & Ge, Hongjuan, 2024. "A safety dynamic evaluation method for missile mission based on multi-layered safety control structure model," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Sun, Hao & Yang, Ming & Zio, Enrico & Li, Xinhong & Lin, Xiaofei & Huang, Xinjie & Wu, Qun, 2024. "A simulation-based approach for resilience assessment of process system: A case of LNG terminal system," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

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