Risk analyses are often performed for economic reasons and safety purposes. In some cases, these studies are biased by epistemic uncertainties due to the lack of information and knowledge, which justifies the need for expert opinion. In such cases, experts can follow different approaches for the elicitation of epistemic data, using probabilistic or imprecise theories. But how do these theories affect the reliability calculation? What are the influences of using a mixture of theories in a multivariable system with a nonexplicit limit model? To answer these questions, we propose an approach for the comparison of these theories, which was performed based on a reliability model using the first-order reliability method (FORM) approach and having the Kitagawa–Takahashi diagram as limit state. We also propose an approach, appropriate to this model, to extend the reliability calculation to variables derived from imprecise probabilities. For the chosen reliability model, obtained results show that there is a certain homogeneity among the considered theories. The study also concludes that priority should be given to expert opinions formulated according to unbounded distributions, in order to achieve better reliability calculation accuracy.

References

1.
Gagnon
,
M.
,
Tahan
,
A.
,
Bocher
,
P.
, and
Thibault
,
D.
,
2013
, “
A Probabilistic Model for the Onset of High Cycle Fatigue (HCF) Crack Propagation: Application to Hydroelectric Turbine Runner
,”
Int. J. Fatigue
,
47
, pp. 
300
307
.10.1016/j.ijfatigue.2012.09.011
2.
Thibault
,
D.
,
Gagnon
,
M.
, and
Godin
,
S.
,
2015
, “
The Effect of Materials Properties on the Reliability of Hydraulic Turbine Runners
,”
Int. J. Fluid Mach. Syst.
,
8
(
4
), pp. 
254
263
.10.5293/IJFMS.2015.8.4.254
3.
Aven
,
T.
,
Baraldi
,
P.
,
Flage
,
R.
, and
Zio
,
E.
,
2014
,
Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods
,
Wiley
,
Hoboken, NJ
.
4.
Ayyub
,
B. M.
,
2001
,
Elicitation of Expert Opinions for Uncertainty and Risks
,
CRC Press
,
Boca Raton, FL
.
5.
O’Hagan
,
A.
,
2012
, “
Probabilistic Uncertainty Specification: Overview, Elaboration Techniques and Their Application to a Mechanistic Model of Carbon Flux
,”
Environ. Modell. Software
,
36
, pp. 
35
48
.10.1016/j.envsoft.2011.03.003
6.
Möller
,
B.
,
Graf
,
W.
,
Beer
,
M.
, and
Sickert
,
J.
,
2001
, “
Fuzzy Probabilistic Method and Its Application for the Safety Assessment of Structures
,”
Proceedings of the European Conference on Computational Mechanics (EECM-2001)
,
Kraków, Poland
.
7.
Limbourg
,
P.
, and
De Rocquigny
,
E.
,
2010
, “
Uncertainty Analysis Using Evidence Theory: Confronting Level-1 and Level-2 Approaches With Data Availability and Computational Constraints
,”
Reliab. Eng. Syst. Saf.
,
95
(
5
), pp. 
550
564
.10.1016/j.ress.2010.01.005
8.
Zadeh
,
L. A.
,
1965
. “
Fuzzy Sets
,”
Inform. Control
,
8
(
3
), pp.
338
353
. 0892-387610.1016/S0019-9958(65)90241-X
9.
Zadeh
,
L. A.
,
1978
, “
Fuzzy sets as a basis for a theory of possibility
,”
Fuzzy Sets Syst.
,
1
(
1
), pp.
3
28
. 0165-011410.1016/0165-0114(78)90029-5
10.
Dubois
,
D.
, and
Prade
,
H.
,
1998
, “
Possibility Theory: Qualitative and Quantitative Aspects
,”
Quantified Representation of Uncertainty and Imprecision
,
Springer
,
Berlin
, pp.
169
226
.
11.
Dubois
,
D.
,
Prade
,
H.
, and
Smets
,
P.
,
2008
, “
A Definition of Subjective Possibility
,”
Int. J. Approx. Reason.
,
48
(
2
), pp.
352
364
.10.1016/j.ijar.2007.01.005
12.
Dubois
,
D.
,
Foulloy
,
L.
,
Mauris
,
G.
, and
Prade
,
H.
,
2004
, “
Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities
,”
Reliable Comput.
,
10
(
4
), pp.
273
297
.10.1023/B:REOM.0000032115.22510.b5
13.
Destercke
,
S.
,
Dubois
,
D.
, and
Chojnacki
,
E.
,
2006
, “
Aggregation of Expert Opinions and Uncertainty Theories
,”
Rencontres Francophones sur la Logique Floue et ses Applications
,
Cepadues
, pp.
295
302
.
14.
Klir
,
G. J.
, and
Smith
,
R. M.
,
2001
, “
On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments
,”
Ann. Math. Artif. Intell.
,
32
(
1–4
), pp.
5
33
.10.1023/A:1016784627561
15.
Baraldi
,
P.
,
Popescu
,
I. C.
, and
Zio
,
E.
,
2010
, “
Methods of Uncertainty Analysis in Prognostics
,”
Int. J. Perform. Eng.
,
6
(
4
), pp.
303
330
.
16.
Yager
,
R. R.
, and
Liu
,
L.
,
2008
,
Classic Works of the Dempster-Shafer Theory of Belief Functions
,
Springer Science & Business Media
,
Berlin
, p.
219
.
17.
Baudrit
,
C.
, and
Dubois
,
D.
,
2005
, “
Comparing Methods for Joint Objective and Subjective Uncertainty Propagation With an Example in a Risk Assessment
,”
ISIPTA
, Vol.
5
.
18.
Beer
,
M.
,
Ferson
,
S.
, and
Kreinovich
,
V.
,
2013
, “
Imprecise Probabilities in Engineering Analyses
,”
Mech. Syst. Sig. Process.
,
37
(
1–2
), pp.
4
29
.10.1016/j.ymssp.2013.01.024
19.
Flage
,
R.
,
Baraldi
,
P.
,
Zio
,
E.
, and
Aven
,
T.
,
2013
, “
Probability and Possibility-Based Representations of Uncertainty in Fault Tree Analysis
,”
Risk Anal.
,
33
(
1
), pp.
121
133
. 0272-433210.1111/risk.2013.33.issue-1
20.
Masson
,
M.-H.
,
2005
, “
Apports de la Théorie des Possibilités et des Fonctions de Croyance à l’analyse de Données Imprécises
,”
Mémoire de Direction de Recherche
, p.
126
.
21.
Ferson
,
S.
,
Joslyn
,
C. A.
,
Helton
,
J. C.
,
Oberkampf
,
W. L.
, and
Sentz
,
K.
,
2004
, “
Summary from the Epistemic Uncertainty Workshop: Consensus Amid Diversity
,”
Reliab. Eng. Syst. Saf.
,
85
(
1
), pp.
355
369
.10.1016/j.ress.2004.03.023
22.
Beck
,
A. T.
,
2016
, “
Strategies for Finding the Design Point under Bounded Random Variables
,”
Struct. Saf.
,
58
, pp.
79
93
.10.1016/j.strusafe.2015.08.006
23.
Castillo
,
E.
,
O’Connor
,
A. J.
,
Nogal
,
M.
, and
Calviño
,
A.
,
2014
, “
On the Physical and Probabilistic Consistency of Some Engineering Random Models
,”
Struct. Saf.
,
51
, pp.
1
12
.10.1016/j.strusafe.2014.05.003
24.
Oussalah
,
M.
,
2000
, “
On the Probability/Possibility Transformations: A Comparative Analysis
,”
Int. J. General Syst.
,
29
(
5
), pp.
671
718
.10.1080/03081070008960969
25.
Cobb
,
B. R.
, and
Shenoy
,
P. P.
,
2006
, “
On the Plausibility Transformation Method for Translating Belief Function Models to Probability Models
,”
Int. J. Approx. Reason.
,
41
(
3
), p
314
330
.10.1016/j.ijar.2005.06.008
26.
Lasserre
,
V.
,
1999
,
Modélisation floue des incertitudes de mesures de capteurs
, Doctoral dissertation,
Chambéry
.
27.
Hahn
,
G. J.
, and
Shapiro
,
S. S.
,
1968
,
Statistical Models in Engineering
,
Wiley
,
New York
.
You do not currently have access to this content.