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 | 2072-2077 |
| Number of pages | 6 |
| ISBN (electronic) | 978-981-11-0745-0 |
| Publication status | Published - 2019 |
| Event | 29th European Safety and Reliability Conference, ESREL 2019 - Leibniz University Hannover, Hannover, Germany Duration: 22 Sept 2019 → 26 Sept 2019 |
Abstract
Uncertainty characterization and propagation through computational models are the two key basic problems in risk and reliability analysis of structures and systems. Commonly used methods are mostly based on precise probability models, which are effective for characterizing the aleatory uncertainty. In real-world applications, the available data of model input variables commonly turn out to be scarce, incomplete and imprecise, and in this case, the epistemic uncertainty also emerges, which prevents us from generating the precise probability models. In this situation, the imprecise probability models such as probability-box model have been developed, and are shown to be especially useful for characterizing these two kinds of uncertainties in a unified framework. However, the performance of the available methods for propagating the imprecise probability models are generally computationally much more expensive than those developed for precise probability model, thus they are not widely used in practical applications. To fill this gap, a new general framework, termed as non-intrusive imprecise stochastic simulation, for efficiently propagating the imprecise probability models, and specifically, for estimating the failure probability bounds, has been developed. In this paper, we inject the line sampling method, which was originally developed for precise stochastic simulation, into the non-intrusive imprecise stochastic simulation framework, so as to further improve the efficiency when applied to low-nonlinear and high-dimensional problems, and to broaden the applicability of this framework. The computational cost of this new development is shown to be the same as the classical line sampling method. The effectiveness of the proposed framework is demonstrated by numerical test examples.
Keywords
- Epistemic uncertainty, Imprecise probability, Imprecise stochastic simulation, Line sampling, Uncertainty quantification
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, 2019. p. 2072-2077.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient propagation of imprecise probability models by imprecise line sampling
AU - Wei, Pengfei
AU - Song, Jingwen
AU - Valdebenito, Marcos A.
AU - Beer, Michael
PY - 2019
Y1 - 2019
N2 - Uncertainty characterization and propagation through computational models are the two key basic problems in risk and reliability analysis of structures and systems. Commonly used methods are mostly based on precise probability models, which are effective for characterizing the aleatory uncertainty. In real-world applications, the available data of model input variables commonly turn out to be scarce, incomplete and imprecise, and in this case, the epistemic uncertainty also emerges, which prevents us from generating the precise probability models. In this situation, the imprecise probability models such as probability-box model have been developed, and are shown to be especially useful for characterizing these two kinds of uncertainties in a unified framework. However, the performance of the available methods for propagating the imprecise probability models are generally computationally much more expensive than those developed for precise probability model, thus they are not widely used in practical applications. To fill this gap, a new general framework, termed as non-intrusive imprecise stochastic simulation, for efficiently propagating the imprecise probability models, and specifically, for estimating the failure probability bounds, has been developed. In this paper, we inject the line sampling method, which was originally developed for precise stochastic simulation, into the non-intrusive imprecise stochastic simulation framework, so as to further improve the efficiency when applied to low-nonlinear and high-dimensional problems, and to broaden the applicability of this framework. The computational cost of this new development is shown to be the same as the classical line sampling method. The effectiveness of the proposed framework is demonstrated by numerical test examples.
AB - Uncertainty characterization and propagation through computational models are the two key basic problems in risk and reliability analysis of structures and systems. Commonly used methods are mostly based on precise probability models, which are effective for characterizing the aleatory uncertainty. In real-world applications, the available data of model input variables commonly turn out to be scarce, incomplete and imprecise, and in this case, the epistemic uncertainty also emerges, which prevents us from generating the precise probability models. In this situation, the imprecise probability models such as probability-box model have been developed, and are shown to be especially useful for characterizing these two kinds of uncertainties in a unified framework. However, the performance of the available methods for propagating the imprecise probability models are generally computationally much more expensive than those developed for precise probability model, thus they are not widely used in practical applications. To fill this gap, a new general framework, termed as non-intrusive imprecise stochastic simulation, for efficiently propagating the imprecise probability models, and specifically, for estimating the failure probability bounds, has been developed. In this paper, we inject the line sampling method, which was originally developed for precise stochastic simulation, into the non-intrusive imprecise stochastic simulation framework, so as to further improve the efficiency when applied to low-nonlinear and high-dimensional problems, and to broaden the applicability of this framework. The computational cost of this new development is shown to be the same as the classical line sampling method. The effectiveness of the proposed framework is demonstrated by numerical test examples.
KW - Epistemic uncertainty
KW - Imprecise probability
KW - Imprecise stochastic simulation
KW - Line sampling
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85089196665&partnerID=8YFLogxK
U2 - 10.3850/978-981-11-2724-3_0994-cd
DO - 10.3850/978-981-11-2724-3_0994-cd
M3 - Conference contribution
AN - SCOPUS:85089196665
SP - 2072
EP - 2077
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 -