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Probabilistic risk assessment based model validation method using Bayesian network

Author

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  • Kwag, Shinyoung
  • Gupta, Abhinav
  • Dinh, Nam

Abstract

Past few decades have seen a rapid growth in the availability of computational power and that induces continually reducing cost of simulation. This rapidly changing scenario together with availability of high precision and large-scale experimental data has enabled development of high fidelity simulation tools capable of simulating multi-physics multi-scale phenomena. At the same time, there has been an increased emphasis on developing strategies for verification and validation of such high fidelity simulation tools. The problem is more pronounced in cases where it is not possible to collect experimental data or field measurements on a large-scale or full scale system performance. This is particularly true in case of systems such as nuclear power plants subjected to external hazards such as earthquakes or flooding. In such cases, engineers rely solely on simulation tools but struggle to establish the credibility of the system level simulations. In practice, validation approaches rely heavily on expert elicitation. There is an increasing need of a quantitative approach for validation of high fidelity simulations that is comprehensive, consistent, and effective. A validation approach should be able to consider uncertainties due to incomplete knowledge and randomness in the system's performance as well as in the characterization of external hazard. A new approach to validation is presented in this paper that uses a probabilistic index as a degree of validation and propagates it through the system using the performance-based probabilistic risk assessment (PRA) framework. Unlike traditional PRA approaches, it utilizes the power of Bayesian statistic to account for non-Boolean relationships and correlations among events at various levels of a network representation of the system. Bayesian updating facilitates evaluation of updated validation information as additional data from experimental observations or improved simulations is incorporated. PRA based framework assists in identifying risk-consistent events and critical path for appropriate allocation of resources to improve the validation.

Suggested Citation

  • Kwag, Shinyoung & Gupta, Abhinav & Dinh, Nam, 2018. "Probabilistic risk assessment based model validation method using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 380-393.
  • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:380-393
    DOI: 10.1016/j.ress.2017.09.013
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    References listed on IDEAS

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    Cited by:

    1. DeJesus Segarra, Jonathan & Bensi, Michelle & Modarres, Mohammad, 2023. "Multi-unit seismic probabilistic risk assessment: A Bayesian network perspective," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Shinyoung Kwag & Jinsung Kwak & Hwanho Lee & Jinho Oh & Gyeong-Hoi Koo, 2019. "Enhancement in the Seismic Performance of a Nuclear Piping System using Multiple Tuned Mass Dampers," Energies, MDPI, vol. 12(11), pages 1-26, May.
    3. Yang, Qing & Zou, Xingqi & Ye, Yunting & Yao, Tao, 2022. "Evaluating the criticality of the product development project portfolio network from the perspective of risk propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    4. Bodda, Saran Srikanth & Gupta, Abhinav & Dinh, Nam, 2020. "Enhancement of risk informed validation framework for external hazard scenario," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Takeda, Satoshi & Kitada, Takanori, 2021. "Simple method based on sensitivity coefficient for stochastic uncertainty analysis in probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Kwag, Shinyoung & Park, Junhee & Choi, In-Kil, 2020. "Development of efficient complete-sampling-based seismic PSA method for nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    7. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Jin, Song & Gong, Jinxin, 2021. "Fragility analysis and probabilistic performance evaluation of nuclear containment structure subjected to internal pressure," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    9. Wang, Chong & Matthies, Hermann G., 2019. "Novel model calibration method via non-probabilistic interval characterization and Bayesian theory," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 84-92.
    10. Kwag, Shinyoung & Choi, Eujeong & Eem, Seunghyun & Ha, Jeong-Gon & Hahm, Daegi, 2021. "Toward improvement of sampling-based seismic probabilistic safety assessment method for nuclear facilities using composite distribution and adaptive discretization," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Liting Jing & Qingqing Xu & Tao Sun & Xiang Peng & Jiquan Li & Fei Gao & Shaofei Jiang, 2020. "Conceptual Scheme Decision Model for Mechatronic Products Driven by Risk of Function Failure Propagation," Sustainability, MDPI, vol. 12(17), pages 1-28, September.
    12. Liming Mu & Yingzhi Zhang & Qiyan Zhang, 2023. "Risk Evaluation Method Based on Fault Propagation and Diffusion," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
    13. Suo, Weilan & Wang, Lin & Li, Jianping, 2021. "Probabilistic risk assessment for interdependent critical infrastructures: A scenario-driven dynamic stochastic model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

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