Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 26 Mai 2019 |
| Veranstaltung | 13th International Conference on Applications of Statistics and Probability in Civil Engineering - Seoul, South Korea, Seoul, Südkorea Dauer: 26 Mai 2019 → 30 Mai 2019 Konferenznummer: 13 |
Konferenz
| Konferenz | 13th International Conference on Applications of Statistics and Probability in Civil Engineering |
|---|---|
| Kurztitel | ICASP13 |
| Land/Gebiet | Südkorea |
| Ort | Seoul |
| Zeitraum | 26 Mai 2019 → 30 Mai 2019 |
Abstract
This paper introduces a practical comparison of a newly introduced inverse method for the quantification of epistemically uncertain model parameters with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo. The paper gives a concise overview of both techniques, and both methods are applied to the quantification of a set of parameters in the well-known DLR Airmod test structure. Specifically, the case where only a very scarce set of experimentally obtained eigenfrequencies and eigenmodes are available is considered. It is shown that for such scarce data, the interval method provides more objective and robust bounds on the uncertain parameters than the Bayesian method, since no prior definition of the uncertainty is required, albeit at the cost that less information on parameter dependency or relative plausibility of different parameter values is obtained.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
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- BibTex
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2019. Beitrag in 13th International Conference on Applications of Statistics and Probability in Civil Engineering, Seoul, Südkorea.
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - Inverse quantification of epistemic uncertainty under scarce data
T2 - 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019
AU - Faes, Matthias
AU - Broggi, Matteo
AU - Patelli, Edoardo
AU - Govers, Yves
AU - Mottershead, John
AU - Beer, Michael
AU - Moens, David
N1 - Conference code: 13
PY - 2019/5/26
Y1 - 2019/5/26
N2 - This paper introduces a practical comparison of a newly introduced inverse method for the quantification of epistemically uncertain model parameters with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo. The paper gives a concise overview of both techniques, and both methods are applied to the quantification of a set of parameters in the well-known DLR Airmod test structure. Specifically, the case where only a very scarce set of experimentally obtained eigenfrequencies and eigenmodes are available is considered. It is shown that for such scarce data, the interval method provides more objective and robust bounds on the uncertain parameters than the Bayesian method, since no prior definition of the uncertainty is required, albeit at the cost that less information on parameter dependency or relative plausibility of different parameter values is obtained.
AB - This paper introduces a practical comparison of a newly introduced inverse method for the quantification of epistemically uncertain model parameters with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo. The paper gives a concise overview of both techniques, and both methods are applied to the quantification of a set of parameters in the well-known DLR Airmod test structure. Specifically, the case where only a very scarce set of experimentally obtained eigenfrequencies and eigenmodes are available is considered. It is shown that for such scarce data, the interval method provides more objective and robust bounds on the uncertain parameters than the Bayesian method, since no prior definition of the uncertainty is required, albeit at the cost that less information on parameter dependency or relative plausibility of different parameter values is obtained.
UR - http://www.scopus.com/inward/record.url?scp=85126508266&partnerID=8YFLogxK
U2 - 10.22725/ICASP13.060
DO - 10.22725/ICASP13.060
M3 - Paper
Y2 - 26 May 2019 through 30 May 2019
ER -