Details
| Original language | English |
|---|---|
| Title of host publication | Vulnerability, Uncertainty, and Risk |
| Subtitle of host publication | Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014 |
| Editors | Jim W. Hall, Siu-Kui Au, Michael Beer |
| Publisher | American Society of Civil Engineers (ASCE) |
| Pages | 320-329 |
| Number of pages | 10 |
| ISBN (electronic) | 9780784413609 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014 - Liverpool, United Kingdom (UK) Duration: 13 Jul 2014 → 16 Jul 2014 |
Abstract
Whether a structure is stable depends on the values of the parameters = (1, n) which describe the structure and its environment. Usually, we know the limit function g() describing stability: a structure is stable if and only if g0. If we also know the probability distribution on the set of all possible combinations , then we can estimate the failure probability P. In practice, we often know that the probability distribution belongs to the known family of distributions (e.g., normal), but we only know the approximate values pi of the parameters pi characterizing the actual distribution. Similarly, we know the family of possible limit functions, but we have only approximate estimates of the parameters corresponding to the actual limit function. In many such situations, we know the accuracy of the corresponding approximations; i.e., we know an upper bound - for which pi-pi. In this case, the only information that we have about the actual (unknown) values of the corresponding parameters p is that p is in the interval [p-, pi +]. Different values pi from the corresponding intervals lead, in general, to different values of the failure probability P. So, under such interval uncertainty, it is desirable to find the range [P, P-]. In this paper, we describe efficient algorithms for computing this range. We also show how to take into account the model inaccuracy, i.e., the fact that the finite-parametric models of the distribution and of the limit function provide only an approximate descriptions of the actual ones.
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
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Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014. ed. / Jim W. Hall; Siu-Kui Au; Michael Beer. American Society of Civil Engineers (ASCE), 2014. p. 320-329.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Efficient Ways of Estimating Failure Probability of Mechanical Structures Under Interval Uncertainty
AU - Beer, M.
AU - de Angelis, M.
AU - Kreinovich, V.
PY - 2014
Y1 - 2014
N2 - Whether a structure is stable depends on the values of the parameters = (1, n) which describe the structure and its environment. Usually, we know the limit function g() describing stability: a structure is stable if and only if g0. If we also know the probability distribution on the set of all possible combinations , then we can estimate the failure probability P. In practice, we often know that the probability distribution belongs to the known family of distributions (e.g., normal), but we only know the approximate values pi of the parameters pi characterizing the actual distribution. Similarly, we know the family of possible limit functions, but we have only approximate estimates of the parameters corresponding to the actual limit function. In many such situations, we know the accuracy of the corresponding approximations; i.e., we know an upper bound - for which pi-pi. In this case, the only information that we have about the actual (unknown) values of the corresponding parameters p is that p is in the interval [p-, pi +]. Different values pi from the corresponding intervals lead, in general, to different values of the failure probability P. So, under such interval uncertainty, it is desirable to find the range [P, P-]. In this paper, we describe efficient algorithms for computing this range. We also show how to take into account the model inaccuracy, i.e., the fact that the finite-parametric models of the distribution and of the limit function provide only an approximate descriptions of the actual ones.
AB - Whether a structure is stable depends on the values of the parameters = (1, n) which describe the structure and its environment. Usually, we know the limit function g() describing stability: a structure is stable if and only if g0. If we also know the probability distribution on the set of all possible combinations , then we can estimate the failure probability P. In practice, we often know that the probability distribution belongs to the known family of distributions (e.g., normal), but we only know the approximate values pi of the parameters pi characterizing the actual distribution. Similarly, we know the family of possible limit functions, but we have only approximate estimates of the parameters corresponding to the actual limit function. In many such situations, we know the accuracy of the corresponding approximations; i.e., we know an upper bound - for which pi-pi. In this case, the only information that we have about the actual (unknown) values of the corresponding parameters p is that p is in the interval [p-, pi +]. Different values pi from the corresponding intervals lead, in general, to different values of the failure probability P. So, under such interval uncertainty, it is desirable to find the range [P, P-]. In this paper, we describe efficient algorithms for computing this range. We also show how to take into account the model inaccuracy, i.e., the fact that the finite-parametric models of the distribution and of the limit function provide only an approximate descriptions of the actual ones.
UR - http://www.scopus.com/inward/record.url?scp=84933558023&partnerID=8YFLogxK
U2 - 10.1061/9780784413609.033
DO - 10.1061/9780784413609.033
M3 - Conference contribution
AN - SCOPUS:84933558023
SP - 320
EP - 329
BT - Vulnerability, Uncertainty, and Risk
A2 - Hall, Jim W.
A2 - Au, Siu-Kui
A2 - Beer, Michael
PB - American Society of Civil Engineers (ASCE)
T2 - 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014
Y2 - 13 July 2014 through 16 July 2014
ER -