Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
| Editors | Michael Beer, Enrico Zio |
| Place of Publication | Singapur |
| Pages | 2622-2627 |
| Number of pages | 6 |
| ISBN (electronic) | 9789811127243 |
| Publication status | Published - 2020 |
| Event | 29th European Safety and Reliability Conference, ESREL 2019 - Leibniz University Hannover, Hannover, Germany Duration: 22 Sept 2019 → 26 Sept 2019 |
Abstract
In the uncertainty quantification and structural reliability evaluation, epistemic uncertainty usually exists simultaneously with the aleatory uncertainty. To characterize these two types of uncertainty is crucial for decision making in structural design. Though extensive investigations have been conducted, resulting in various approaches, including, e.g., the fuzzy analysis method, the imprecise probability and empirically based probabilistic method, etc., a compatible framework with efficient implementation is still in need. Actually, due to limited available data, the distribution parameters (e.g. mean value and standard deviation) carry epistemic uncertainty, thus the basic random variables should be characterized by a family of probability distributions, rather than an uniquely specified probability distribution. This set of distribution parameters can be determined by the bootstrap method. Such problem is addressed in the present paper. Moreover, to improve the computational efficiency, a newly proposed method called PDEM-COM is adopted, without compromising numerical accuracy. Numerical applications are illustrated to indicate the feasibility of PDEM-COM framework for quantification of epistemic uncertainty. Moreover, this basic idea can also be extended to quantification of epistemic uncertainty due to other sources.
Keywords
- Change of probability measure, Epistemic uncertainty, Nonlinear structures, PDEM
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
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Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. ed. / Michael Beer; Enrico Zio. Singapur, 2020. p. 2622-2627.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A PDEM-COM framework for quantification of epistemic uncertainty
AU - Wan, Zhiqiang
AU - Chen, Jianbing
AU - Li, Jie
AU - Beer, Michael
N1 - Funding information: Financial supports from the National Science Fund for Distinguished Young Scholars of China (Grant No.51725804), National Science Fund of China (Grant No.51538010) and the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No.11761131014) are gratefully appreciated.
PY - 2020
Y1 - 2020
N2 - In the uncertainty quantification and structural reliability evaluation, epistemic uncertainty usually exists simultaneously with the aleatory uncertainty. To characterize these two types of uncertainty is crucial for decision making in structural design. Though extensive investigations have been conducted, resulting in various approaches, including, e.g., the fuzzy analysis method, the imprecise probability and empirically based probabilistic method, etc., a compatible framework with efficient implementation is still in need. Actually, due to limited available data, the distribution parameters (e.g. mean value and standard deviation) carry epistemic uncertainty, thus the basic random variables should be characterized by a family of probability distributions, rather than an uniquely specified probability distribution. This set of distribution parameters can be determined by the bootstrap method. Such problem is addressed in the present paper. Moreover, to improve the computational efficiency, a newly proposed method called PDEM-COM is adopted, without compromising numerical accuracy. Numerical applications are illustrated to indicate the feasibility of PDEM-COM framework for quantification of epistemic uncertainty. Moreover, this basic idea can also be extended to quantification of epistemic uncertainty due to other sources.
AB - In the uncertainty quantification and structural reliability evaluation, epistemic uncertainty usually exists simultaneously with the aleatory uncertainty. To characterize these two types of uncertainty is crucial for decision making in structural design. Though extensive investigations have been conducted, resulting in various approaches, including, e.g., the fuzzy analysis method, the imprecise probability and empirically based probabilistic method, etc., a compatible framework with efficient implementation is still in need. Actually, due to limited available data, the distribution parameters (e.g. mean value and standard deviation) carry epistemic uncertainty, thus the basic random variables should be characterized by a family of probability distributions, rather than an uniquely specified probability distribution. This set of distribution parameters can be determined by the bootstrap method. Such problem is addressed in the present paper. Moreover, to improve the computational efficiency, a newly proposed method called PDEM-COM is adopted, without compromising numerical accuracy. Numerical applications are illustrated to indicate the feasibility of PDEM-COM framework for quantification of epistemic uncertainty. Moreover, this basic idea can also be extended to quantification of epistemic uncertainty due to other sources.
KW - Change of probability measure
KW - Epistemic uncertainty
KW - Nonlinear structures
KW - PDEM
UR - http://www.scopus.com/inward/record.url?scp=85089175302&partnerID=8YFLogxK
U2 - 10.3850/978-981-11-2724-3_0969-cd
DO - 10.3850/978-981-11-2724-3_0969-cd
M3 - Conference contribution
AN - SCOPUS:85089175302
SP - 2622
EP - 2627
BT - Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
A2 - Beer, Michael
A2 - Zio, Enrico
CY - Singapur
T2 - 29th European Safety and Reliability Conference, ESREL 2019
Y2 - 22 September 2019 through 26 September 2019
ER -