Event tree analysis (ETA) is an established risk analysis technique to assess likelihood (in a probabilistic context) of an accident. The objective data available to estimate the likelihood is often missing (or sparse), and even if available, is subject to incompleteness (partial ignorance) and imprecision (vagueness). Without addressing incompleteness and imprecision in the available data, ETA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. This paper explores two approaches to address data uncertainties, namely, fuzzy sets and evidence theory, and compares the results with Monte Carlo simulations. A fuzzy-based approach is used for handling imprecision and subjectivity, whereas evidence theory is used for handling inconsistent, incomplete and conflicting data. Application of these approaches in ETA is demonstrated using the example of an LPG release near a processing facility. © 2009 The Institution of Chemical Engineers.

Item Type:	Refereed Article
Keywords:	Data uncertainties; Event tree analysis; Evidence theory; Fuzzy-based approach; Monte Carlo simulations; Data uncertainties; Event tree analysis; Evidence theory; Fuzzy-based approach; Monte Carlo simulations; Computer simulation languages
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, FI (Professor Faisal Khan)
ID Code:	94425
Year Published:	2009
Web of Science® Times Cited:	79
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2014-09-09
Last Modified:	2015-01-27
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