Paper Info
Title
Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application.
Abstract
The use of expert systems can be helpful to improve the transparency and repeatability of assessments in areas of risk analysis with limited data available. In this field, human reliability analysis (HRA) is no exception, and, in particular, dependence analysis is an HRA task strongly based on analyst judgement. The analysis of dependence among Human Failure Events refers to the assessment of the effect of an earlier human failure on the probability of the subsequent ones. This paper analyses and compares two expert systems, based on Bayesian Belief Networks and Fuzzy Logic (a Fuzzy Expert System, FES), respectively. The comparison shows that a BBN approach should be preferred in all the cases characterized by quantifiable uncertainty in the input (i.e. when probability distributions can be assigned to describe the input parameters uncertainty), since it provides a satisfactory representation of the uncertainty and its output is directly interpretable for use within PSA. On the other hand, in cases characterized by very limited knowledge, an analyst may feel constrained by the probabilistic framework, which requires assigning probability distributions for describing uncertainty. In these cases, the FES seems to lead to a more transparent representation of the input and output uncertainty.
Year
DOI
Venue
2015
10.1016/j.ress.2015.01.016
Reliability Engineering & System Safety
Keywords
DocType
Volume
Expert judgement,Expert models,Bayesian belief networks,Fuzzy logic,Human reliability analysis,Dependence assessment
Journal
138
ISSN
Citations 
PageRank 
0951-8320
11
0.65
References 
Authors
13
5
Name
Order
Citations
PageRank
Piero Baraldi123621.96
Luca Podofillini238331.44
Lusine Mkrtchyan3110.65
Enrico Zio4110.65
Vinh N Dang5647.22