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Machine learning of fire hazard model simulations for use in probabilistic safety assessments at nuclear power plants

Author

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  • Worrell, Clarence
  • Luangkesorn, Louis
  • Haight, Joel
  • Congedo, Thomas

Abstract

This study explored the application of machine learning to generate metamodel approximations of a physics-based fire hazard model. The motivation to generate accurate and efficient metamodels is to improve modeling realism in probabilistic safety assessments where computational burden has prevented broader application of high fidelity models. The process involved scenario definition, generating training data by iteratively running the fire hazard model called CFAST over a range of input space using the RAVEN software, exploratory data analysis and feature selection, an initial testing of a broad set of metamodel methods, and finally metamodel selection and tuning using the R software.

Suggested Citation

  • Worrell, Clarence & Luangkesorn, Louis & Haight, Joel & Congedo, Thomas, 2019. "Machine learning of fire hazard model simulations for use in probabilistic safety assessments at nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 128-142.
    • Handle: RePEc:eee:reensy:v:183:y:2019:i:c:p:128-142
    DOI: 10.1016/j.ress.2018.11.014
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    Cited by:

    1. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Seo, Seung-Kwon & Yoon, Young-Gak & Lee, Ju-sung & Na, Jonggeol & Lee, Chul-Jin, 2022. "Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    4. Robinson, Allen C. & Drake, Richard R. & Swan, M. Scot & Bennett, Nichelle L. & Smith, Thomas M. & Hooper, Russell & Laity, George R., 2021. "A software environment for effective reliability management for pulsed power design," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    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. Simsekler, Mecit Can Emre & Rodrigues, Clarence & Qazi, Abroon & Ellahham, Samer & Ozonoff, Al, 2021. "A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 208(C).

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