Uncertainty representation and propagation in quantified risk assessment using fuzzy sets

Quelch J. and Cameron I.T. (1994) Uncertainty representation and propagation in quantified risk assessment using fuzzy sets. Journal of Loss Prevention in the Process Industries, 7 6: 463-473. doi:10.1016/0950-4230(94)80004-9


Author Quelch J.
Cameron I.T.
Title Uncertainty representation and propagation in quantified risk assessment using fuzzy sets
Journal name Journal of Loss Prevention in the Process Industries   Check publisher's open access policy
ISSN 0950-4230
Publication date 1994-01-01
Sub-type Article (original research)
DOI 10.1016/0950-4230(94)80004-9
Open Access Status Not yet assessed
Volume 7
Issue 6
Start page 463
End page 473
Total pages 11
Subject 1504 Commercial Services
1508 Process Chemistry and Technology
2213 Safety, Risk, Reliability and Quality
Abstract It is generally acknowledged that there are substantial uncertainties present in any analysis of risk. This paper provides a brief overview of the current techniques used for uncertainty analyses, and highlights their inappropriateness for practical use in the complete risk assessment process. The concept of fuzzy sets as a means for quantifying uncertainty is introduced and a case study demonstrates the application of this method to a simple consequence analysis where parameter uncertainty is considered. The results of this fuzzy analysis are compared with those of a more traditional probabilistic approach using a Monte Carlo simulation. This comparison demonstrates that the novel approach of fuzzy sets is a more appropriate technique due to its non-statistical nature and that the amount of computation required is substantially reduced compared to the traditional probabilistic approach. The versatility of fuzzy set theory suggests that this approach could also be used to quantify other types of uncertainty present in the risk assessment process, including model uncertainty and expert opinion.
Keyword fuzzy sets
quantified risk assessment
uncertainty analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: Scopus Import - Archived
 
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