Treating Uncertainties in a Nuclear Seismic Probabilistic Risk Assessment by Means of the Dempster-Shafer Theory of Evidence

Lo, Chung-Kung; Pedroni, Nicola; Zio, Enrico

doi:10.5516/net.03.2014.701

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…For example, a flooding analysis may use a computation fluid dynamics (CFD) or a finite element (FE) model [4,17,18,19]. Multiple simulation-based fragility curves can be generated by including epistemic uncertainty for the simulation median capacity (or median of the simulated lognormal fragility function) as shown in Figure 2 (a) [20,21]. In the performance-based risk-informed validation framework, binary trees (fault and event trees) are mapped into a Bayesian network using a mapping algorithm [22].…”

Section: Performance-based Risk Informed Validation Frameworkmentioning

confidence: 99%

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Risk-Informed Decision-Making in Verification and Validation Under Uncertainty

Bodda,

Gupta

2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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In recent years, the use of advanced simulation tools for modeling the behavior of flooding at a nuclear power plant has gained significant importance. The credibility of advanced simulation codes is assessed using formal approaches for verification and validation. This paper proposes a formal risk-informed validation approach that provides a basis to quantify the credibility of system-level risk assessments. The credibility of system-level validation is represented using a probabilistic metric and maturity levels. The applicability of the proposed framework is evaluated by application to a flooding scenario of a sunny day dam failure and is represented in terms of various maturity levels and helps in the process of decision-making.

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Section: Performance-based Risk Informed Validation Frameworkmentioning

confidence: 99%

“…Multiple simulation fragility curves are generated by assuming a coefficient of variation of 0.1 for the simulation median capacity. Similarly, multiple data-driven fragility curves are generated by including data applicability [2,20,21].…”

Section: Safe-binary/cliff-edge Conditionmentioning

confidence: 99%

Risk-Informed Decision-Making in Verification and Validation Under Uncertainty

Bodda,

Gupta

2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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“…Structural analysis methods are further discussed in Section 2.3. Uncertainty can also be modeled using probabilistic methods such as Bayesian statistics [26,27], which combines experts' judgment and scarce data, or non-probabilistic methods such as the Dempster-Shafer theory [28] of evidence or fuzzy sets [29]. The choice is mostly driven by the availability of data, hence when sufficient data are available, as assumed to be the case in PSDA, statistical methods based on numerical models or data are more favorable [30].…”

Section: Treatment Of Uncertainty In Pbeementioning

confidence: 99%

A Holistic Review of GM/IM Selection Methods from a Structural Performance-Based Perspective

Esteghamati

2022

Sustainability

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Current fully probabilistic approaches to performance-based earthquake engineering describe structures’ behavior under a wide range of seismic hazard levels. These approaches require a detailed representation of ground motion (GM) uncertainty at all considered hazard levels, yet different GM selection methods lead to different estimations of structural performance. This paper presents a holistic review of the current practices in GM representation and selection for structural demand analysis through a performance-based lens. The multidisciplinary nature of GM selection, ranging from earth science to engineering seismology and statistics, has created a preponderance of literature to find the best practice for probabilistic assessment of structures in terms of computational efficiency and statistical accuracy. Many of these studies focus individually on GM selection or structural analysis, and the relatively scarce review papers either focus on code-based GM selection or do not specifically address risk-based evaluations by overlooking the interaction between GM selection and structural analysis. This paper aims to aid researchers in selecting appropriate GMs as part of a statistically valid and robust probabilistic demand analysis without performing an exhaustive literature review. Discussion on the available computational tools and their trade-offs for risk-based assessment of single structures is provided. While the problem-specific nature of GM selection means that no pre-selected set of GM/IM is applicable to all cases, the comprehensive narrative of this paper is expected to aid analysts in reaching a more informed decision.

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“…For example, in Cruz et al (2009), a worst case analysis of tsunamis impacting an oil refinery is reported. In Prasad (2012) and Lo (2014), applications to a nuclear power plant are described. However, these approaches have proven to be limited in modelling seismic sources as well as tsunamis due to the large uncertainty given by the scarcity of tsunami observations (Geist & Parsons, 2014).…”

Section: Acronymsmentioning

confidence: 99%