Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment. Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment. © 2011 Elsevier Ltd.

Item Type:	Refereed Article
Keywords:	Bow-tie; Expert knowledge; Fuzzy sets and evidence theory; Risk assessment; Uncertainty; Updating; Bow tie; Evidence theories; Expert knowledge; Uncertainty; Updating; Control system analysis; Fuzzy sets; Industrial applications; Rating
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Ocean engineering
Objective Division:	Mineral Resources (Excl. Energy Resources)
Objective Group:	Environmentally sustainable mineral resource activities
Objective Field:	Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:	Khan, F (Professor Faisal Khan)
ID Code:	94447
Year Published:	2012
Web of Science® Times Cited:	85
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2014-09-09
Last Modified:	2017-11-03
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