Details
| Original language | English |
|---|---|
| Pages (from-to) | 174-200 |
| Number of pages | 27 |
| Journal | International Journal of Reliability and Safety |
| Volume | 3 |
| Issue number | 1-3 |
| Publication status | Published - 2009 |
| Externally published | Yes |
Abstract
In this paper, the specification of fuzzy random quantities is considered for selected cases of problematic information as it appears frequently in engineering practice. The problem of inconsistency regarding uncertainty and imprecision is addressed. Quantification strategies are proposed for the following cases: (i) samples of small size (ii) samples with imprecise elements and (iii) samples obtained under inconsistent environmental conditions. Typical expert knowledge is included in the considerations. For solution, traditional statistical methods are combined with non-stochastic models for dealing with imprecision. Statistical uncertainty and imprecision are reflected separately in the quantification results. The entire range of possible stochastic models is covered and can be forwarded to a structural analysis and reliability assessment. This provides valuable information for subsequent decision-making. The risk of deriving wrong decisions due to biased or narrowed uncertainty quantification can be reduced significantly. The proposed quantification strategies are demonstrated by way of numerical examples.
Keywords
- Fuzzy methods, Fuzzy probabilities, Imprecise data, Inconsistent data, Reliability assessment, Uncertain structural analysis
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
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In: International Journal of Reliability and Safety, Vol. 3, No. 1-3, 2009, p. 174-200.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Engineering quantification of inconsistent information
AU - Beer, Michael
PY - 2009
Y1 - 2009
N2 - In this paper, the specification of fuzzy random quantities is considered for selected cases of problematic information as it appears frequently in engineering practice. The problem of inconsistency regarding uncertainty and imprecision is addressed. Quantification strategies are proposed for the following cases: (i) samples of small size (ii) samples with imprecise elements and (iii) samples obtained under inconsistent environmental conditions. Typical expert knowledge is included in the considerations. For solution, traditional statistical methods are combined with non-stochastic models for dealing with imprecision. Statistical uncertainty and imprecision are reflected separately in the quantification results. The entire range of possible stochastic models is covered and can be forwarded to a structural analysis and reliability assessment. This provides valuable information for subsequent decision-making. The risk of deriving wrong decisions due to biased or narrowed uncertainty quantification can be reduced significantly. The proposed quantification strategies are demonstrated by way of numerical examples.
AB - In this paper, the specification of fuzzy random quantities is considered for selected cases of problematic information as it appears frequently in engineering practice. The problem of inconsistency regarding uncertainty and imprecision is addressed. Quantification strategies are proposed for the following cases: (i) samples of small size (ii) samples with imprecise elements and (iii) samples obtained under inconsistent environmental conditions. Typical expert knowledge is included in the considerations. For solution, traditional statistical methods are combined with non-stochastic models for dealing with imprecision. Statistical uncertainty and imprecision are reflected separately in the quantification results. The entire range of possible stochastic models is covered and can be forwarded to a structural analysis and reliability assessment. This provides valuable information for subsequent decision-making. The risk of deriving wrong decisions due to biased or narrowed uncertainty quantification can be reduced significantly. The proposed quantification strategies are demonstrated by way of numerical examples.
KW - Fuzzy methods
KW - Fuzzy probabilities
KW - Imprecise data
KW - Inconsistent data
KW - Reliability assessment
KW - Uncertain structural analysis
UR - http://www.scopus.com/inward/record.url?scp=77957357107&partnerID=8YFLogxK
U2 - 10.1504/IJRS.2009.026840
DO - 10.1504/IJRS.2009.026840
M3 - Article
AN - SCOPUS:77957357107
VL - 3
SP - 174
EP - 200
JO - International Journal of Reliability and Safety
JF - International Journal of Reliability and Safety
SN - 1479-389X
IS - 1-3
ER -