Treatment of uncertainty in Probabilistic Risk Assessment using Monte Carlo analysis

Innal, Fares; Chebila, Mourad; Bourareche, Mouloud; Mohamed, Antar Si

doi:10.1109/icosc.2013.6750944

Cited by 4 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The matter that shows the important benefit in terms of computational cost can be gained by employing PCE. However, the MC results given in [23] look a little different especially for the standard deviation. The matter that can be referred to the dissimilarity of the used codes including the utilized sampling technique.…”

Section: Implementation For Accident Frequency Analysismentioning

confidence: 86%

“…Such scenarios are treated in [23] by employing a Bow-Tie model to show the relationship between the various involved events and their escalation to generate such accidents. Actually, the previously described components (i.e., LE, LIC, LICV, LT, SV, PCV and PC) are considered periodically tested and instantaneously repaired; therefore, only their relating failure rates (λ) and test intervals (T) are taken into account in the analysis.…”

Section: Implementation For Accident Frequency Analysismentioning

confidence: 99%

“…Data relating to the involved input parameters is given in Table 2, which was adopted from [23]. An approximated surrogate model ( ) is created using the discussed approach of PCE to replace the model given in Equation (11) for the purpose of analyzing the propagation of those uncertain quantities.…”

Section: Implementation For Accident Frequency Analysismentioning

confidence: 99%

See 2 more Smart Citations

Non-Intrusive Polynomial Chaos for a Realistic Estimation of Accident Frequency

Chebila¹

2019

Int J Performability Eng

Self Cite

View full text Add to dashboard Cite

The pivotal role of frequency analysis in quantitative risk assessments necessitates realistic and efficient prediction of their metrics. Uncertainty analysis, which aims to determine the effects of the involved input uncertainties on the output of interest form a strong basis for all the decisions to be taken based on the results of such assessments. The main purpose of this paper is to employ a non-intrusive polynomial chaos approach to simplify the model relating to the estimation of the frequency of major accidents. Such application facilitates the propagation of the associated parametric uncertainties and makes the whole process less expensive while assuring the desired accuracy when compared with the classical application of the Monte Carlo simulation.

show abstract

Section: Implementation For Accident Frequency Analysismentioning

confidence: 86%

Section: Implementation For Accident Frequency Analysismentioning

confidence: 99%

See 1 more Smart Citation

Non-Intrusive Polynomial Chaos for a Realistic Estimation of Accident Frequency

Chebila¹

2019

Int J Performability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sometimes, risk is measured through the assessment of the probability of occurrence of an undesired event and the magnitude of consequences [2]. In this way, risk assessment encompasses the answers to the following questions [3]:…”

Section: Basics Of Risk Assessmentmentioning

confidence: 99%

Treatment of Uncertainties in Probabilistic Risk Assessment

Vasconcelos¹,

Soares²,

Costa³

et al. 2020

Reliability and Maintenance - An Overview of Cases

View full text Add to dashboard Cite

Probabilistic risk assessment (PRA), sometimes called probabilistic safety analysis, quantifies the risk of undesired events in industrial facilities. However, one of the weaknesses that undermines the credibility and usefulness of this technique is the uncertainty in PRA results. Fault tree analysis (FTA) and event tree analysis (ETA) are the most important PRA techniques for evaluating system reliabilities and likelihoods of accident scenarios. Uncertainties, as incompleteness and imprecision, are present in probabilities of undesired events and failure rate data. Furthermore, both FTA and ETA traditionally assume that events are independent, assumptions that are often unrealistic and introduce uncertainties in data and modeling when using FTA and ETA. This work explores uncertainty handling approaches for analyzing the fault trees and event trees (method of moments) as a way to overcome the challenges of PRA. Applications of the developed frameworks and approaches are explored in illustrative examples, where the probability distributions of the top event of fault trees are obtained through the propagation of uncertainties of the failure probabilities of basic events. The application of the method of moments to propagate uncertainty of log-normal distributions showed good agreement with results available in the literature using different methods.

show abstract