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Strengthening quantitative risk assessments by systematic treatment of uncertain assumptions

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  • Berner, C.
  • Flage, R.

Abstract

The results of quantitative risk assessments (QRA) are conditional on the background knowledge on which the assessments are based, including phenomenological understanding, models, data and expert statements used, as well as assumptions made. Risk indices established in the risk assessment, such as individual risk numbers and f–N curves, may have a more or less solid foundation, depending for example on the validity of assumptions made. Poor models, lack of data or simplistic assumptions are examples of potential sources of uncertainty “hidden in the background knowledge†of a risk assessment. These uncertainties need to be reflected in the risk assessment. Recently, a method for treating uncertain assumptions in a QRA was suggested. The method is based on the different settings faced when making assumptions in risk assessments, considering beliefs about assumption deviation, sensitivity of the risk index to changes in the assumption, and the overall strength of knowledge involved. In the present paper we apply, test and adjust the method using a risk assessment of a lifting operation related to the oil and gas industry as a case. We find that an adjusted version of the method provides systematic guidance on how to treat uncertainties in a QRA.

Suggested Citation

  • Berner, C. & Flage, R., 2016. "Strengthening quantitative risk assessments by systematic treatment of uncertain assumptions," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 46-59.
    • Handle: RePEc:eee:reensy:v:151:y:2016:i:c:p:46-59
    DOI: 10.1016/j.ress.2015.10.009
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    References listed on IDEAS

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    2. Flage, Roger & Askeland, Tore, 2020. "Assumptions in quantitative risk assessments: When explicit and when tacit?," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Langdalen, Henrik & Abrahamsen, Eirik Bjorheim & Abrahamsen, HÃ¥kon Bjorheim, 2020. "A New Framework To Idenitfy And Assess Hidden Assumptions In The Background Knowledge Of A Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    4. Berner, Christine Louise & Flage, Roger, 2017. "Creating risk management strategies based on uncertain assumptions and aspects from assumption-based planning," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 10-19.
    5. Juntao Zhang & Hyungju Kim & Yiliu Liu & Mary Ann Lundteigen, 2019. "Combining system-theoretic process analysis and availability assessment: A subsea case study," Journal of Risk and Reliability, , vol. 233(4), pages 520-536, August.
    6. Bjørnsen, Kjartan & Selvik, Jon Tømmerås & Aven, Terje, 2019. "A semi-quantitative assessment process for improved use of the expected value of information measure in safety management," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 494-502.
    7. Zhenhui Liu & Ajit Kumar Verma, 2022. "A novel toolbox for dropped object hit probability evaluation and orientation optimization of subsea lines," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1705-1713, August.
    8. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Tasneem Bani-Mustafa & Nicola Pedroni & Enrico Zio & Dominique Vasseur & Francois Beaudouin, 2020. "A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models," Journal of Risk and Reliability, , vol. 234(6), pages 748-763, December.
    10. Flage, Roger & Aven, Terje & Berner, Christine L., 2018. "A comparison between a probability bounds analysis and a subjective probability approach to express epistemic uncertainties in a risk assessment context – A simple illustrative example," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 1-10.
    11. Berner, Christine Louise & Flage, Roger, 2016. "Comparing and integrating the NUSAP notational scheme with an uncertainty based risk perspective," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 185-194.

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