Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics |
| Editors | W. Desmet, B. Pluymers, D. Moens, J. del Fresno Zarza |
| Pages | 4302-4313 |
| Number of pages | 12 |
| ISBN (electronic) | 9789082893175 |
| Publication status | Published - 9 Sept 2024 |
| Event | 31st International Conference on Noise and Vibration Engineering, ISMA 2024 and 10th International Conference on Uncertainty in Structural Dynamics, USD 2024 - Leuven, Belgium Duration: 9 Sept 2024 → 11 Sept 2024 |
Publication series
| Name | Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics |
|---|
Abstract
Bayesian updating reduces epistemic uncertainty for more reliable predictions, but characterizing the distribution of conditional failure probability with measurement data is complex. This study proposes an efficient and accurate method to fully describe the probabilistic characteristics of the updated conditional failure probability. It formulates the first three raw moments of the updated conditional reliability index and uses weighted sparse grid numerical integration to evaluate these moments. A shifted lognormal distribution is then used to approximate the probability density function of the updated conditional reliability index, allowing for the determination of the mean, quantiles, and distribution of the updated conditional failure probability with information reuse. An illustrative example was conducted to demonstrate the method's performance, with results compared against benchmarks from MCMC combined with MCS.
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Mechanics of Materials
- Physics and Astronomy(all)
- Acoustics and Ultrasonics
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Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics. ed. / W. Desmet; B. Pluymers; D. Moens; J. del Fresno Zarza. 2024. p. 4302-4313 (Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Bayesian updating of conditional failure probability using method of moments
AU - Li, P. P.
AU - Zhao, Y. G.
AU - Dang, C.
AU - Broggi, M.
AU - Valdebenito, M. A.
AU - Faes, M. G.R.
N1 - Publisher Copyright: © 2024 Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.
PY - 2024/9/9
Y1 - 2024/9/9
N2 - Bayesian updating reduces epistemic uncertainty for more reliable predictions, but characterizing the distribution of conditional failure probability with measurement data is complex. This study proposes an efficient and accurate method to fully describe the probabilistic characteristics of the updated conditional failure probability. It formulates the first three raw moments of the updated conditional reliability index and uses weighted sparse grid numerical integration to evaluate these moments. A shifted lognormal distribution is then used to approximate the probability density function of the updated conditional reliability index, allowing for the determination of the mean, quantiles, and distribution of the updated conditional failure probability with information reuse. An illustrative example was conducted to demonstrate the method's performance, with results compared against benchmarks from MCMC combined with MCS.
AB - Bayesian updating reduces epistemic uncertainty for more reliable predictions, but characterizing the distribution of conditional failure probability with measurement data is complex. This study proposes an efficient and accurate method to fully describe the probabilistic characteristics of the updated conditional failure probability. It formulates the first three raw moments of the updated conditional reliability index and uses weighted sparse grid numerical integration to evaluate these moments. A shifted lognormal distribution is then used to approximate the probability density function of the updated conditional reliability index, allowing for the determination of the mean, quantiles, and distribution of the updated conditional failure probability with information reuse. An illustrative example was conducted to demonstrate the method's performance, with results compared against benchmarks from MCMC combined with MCS.
UR - http://www.scopus.com/inward/record.url?scp=85212187061&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85212187061
T3 - Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics
SP - 4302
EP - 4313
BT - Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics
A2 - Desmet, W.
A2 - Pluymers, B.
A2 - Moens, D.
A2 - del Fresno Zarza, J.
T2 - 31st International Conference on Noise and Vibration Engineering, ISMA 2024 and 10th International Conference on Uncertainty in Structural Dynamics, USD 2024
Y2 - 9 September 2024 through 11 September 2024
ER -