IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v29y2009i5p662-675.html
   My bibliography  Save this article

Uncertainty Analysis Based on Probability Bounds (P‐Box) Approach in Probabilistic Safety Assessment

Author

Listed:
  • Durga Rao Karanki
  • Hari Shankar Kushwaha
  • Ajit Kumar Verma
  • Srividya Ajit

Abstract

A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision‐making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster‐Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level‐1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p‐boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two‐phase Monte Carlo simulation results.

Suggested Citation

  • Durga Rao Karanki & Hari Shankar Kushwaha & Ajit Kumar Verma & Srividya Ajit, 2009. "Uncertainty Analysis Based on Probability Bounds (P‐Box) Approach in Probabilistic Safety Assessment," Risk Analysis, John Wiley & Sons, vol. 29(5), pages 662-675, May.
  • Handle: RePEc:wly:riskan:v:29:y:2009:i:5:p:662-675
    DOI: 10.1111/j.1539-6924.2009.01221.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.2009.01221.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1539-6924.2009.01221.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jon C. Helton, 1994. "Treatment of Uncertainty in Performance Assessments for Complex Systems," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 483-511, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Lunhu & Kang, Rui & Pan, Xing & Zuo, Dujun, 2020. "Risk assessment of uncertain random system—Level-1 and level-2 joint propagation of uncertainty and probability in fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    2. Roger Flage & Piero Baraldi & Enrico Zio & Terje Aven, 2013. "Probability and Possibility‐Based Representations of Uncertainty in Fault Tree Analysis," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 121-133, January.
    3. Yakov Ben‐Haim, 2012. "Doing Our Best: Optimization and the Management of Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1326-1332, August.
    4. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
    5. Daniel J. Rozell & Sheldon J. Reaven, 2012. "Water Pollution Risk Associated with Natural Gas Extraction from the Marcellus Shale," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1382-1393, August.
    6. He, Rui & Zhu, Jingyu & Chen, Guoming & Tian, Zhigang, 2022. "A real-time probabilistic risk assessment method for the petrochemical industry based on data monitoring," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Roger Flage & Terje Aven & Enrico Zio & Piero Baraldi, 2014. "Concerns, Challenges, and Directions of Development for the Issue of Representing Uncertainty in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1196-1207, July.
    8. Fan Yang & Zhufeng Yue & Lei Li & Dong Guan, 2018. "Hybrid reliability-based multidisciplinary design optimization with random and interval variables," Journal of Risk and Reliability, , vol. 232(1), pages 52-64, February.
    9. McKeand, Austin M. & Gorguluarslan, Recep M. & Choi, Seung-Kyum, 2021. "Stochastic analysis and validation under aleatory and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    10. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.
    11. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A Critical Discussion and Practical Recommendations on Some Issues Relevant to the Nonprobabilistic Treatment of Uncertainty in Engineering Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1315-1340, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    2. J. C. Helton & F. J. Davis, 2002. "Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 591-622, June.
    3. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2020. "Property values associated with the failure of individual links in a system with multiple weak and strong links," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Aven, Terje, 2020. "Three influential risk foundation papers from the 80s and 90s: Are they still state-of-the-art?," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    6. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    7. Terje Aven, 2010. "On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 354-360, March.
    8. Julia J. Pet‐Armacost & Jose Sepulveda & Milton Sakude, 1999. "Monte Carlo Sensitivity Analysis of Unknown Parameters in Hazardous Materials Transportation Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 19(6), pages 1173-1184, December.
    9. Terje Aven, 2011. "On Different Types of Uncertainties in the Context of the Precautionary Principle," Risk Analysis, John Wiley & Sons, vol. 31(10), pages 1515-1525, October.
    10. Stephen E. Chick, 2001. "Input Distribution Selection for Simulation Experiments: Accounting for Input Uncertainty," Operations Research, INFORMS, vol. 49(5), pages 744-758, October.
    11. M. Elisabeth Paté-Cornell & Robin L. Dillon, 2006. "The Respective Roles of Risk and Decision Analyses in Decision Support," Decision Analysis, INFORMS, vol. 3(4), pages 220-232, December.
    12. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2020. "Margins associated with loss of assured safety for systems with multiple weak links and strong links," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    13. Andrea Saltelli & Stefano Tarantola & Karen Chad, 1998. "Presenting Results from Model Based Studies to Decision‐Makers: Can Sensitivity Analysis Be a Defogging Agent?," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 799-803, December.
    14. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Ivan Lizaga & Borja Latorre & Leticia Gaspar & Ana Navas, 2020. "FingerPro: an R Package for Tracking the Provenance of Sediment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3879-3894, September.
    16. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2022. "Probability of Loss of Assured Safety in Systems with Multiple Time-Dependent Failure Modes: Incorporation of Delayed Link Failure in the Presence of Aleatory Uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    17. Emanuele Borgonovo, 2008. "Epistemic Uncertainty in the Ranking and Categorization of Probabilistic Safety Assessment Model Elements: Issues and Findings," Risk Analysis, John Wiley & Sons, vol. 28(4), pages 983-1001, August.
    18. Amirhossein Mokhtari & H. Christopher Frey, 2005. "Sensitivity Analysis of a Two‐Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1511-1529, December.
    19. Victor R. Vasquez & Wallace B. Whiting, 2005. "Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements with Monte Carlo Methods," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1669-1681, December.
    20. M. Elisabeth Paté‐Cornell, 1999. "Conditional Uncertainty Analysis and Implications for Decision Making: The Case of WIPP," Risk Analysis, John Wiley & Sons, vol. 19(5), pages 995-1002, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:29:y:2009:i:5:p:662-675. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.