Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
| Editors | Michael Beer, Enrico Zio, Kok-Kwang Phoon, Bilal M. Ayyub |
| Pages | 498-501 |
| Number of pages | 4 |
| Publication status | Published - 2022 |
| Event | 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 - Hannover, Germany Duration: 4 Sept 2022 → 7 Sept 2022 |
Publication series
| Name | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
|---|
Abstract
This paper presents a highly efficient and effective approach to bound the first excursion probability of linear stochastic FE models subjected to imprecise Gaussian excitations. In previous work, some of the authors proposed a highly efficient approach based on the operator norm framework to bound such first excursion probabilities without having to resort to double-loop problems [1]. However very efficient, the approach presented in [1] is limited to deterministic models, or models containing epistemic uncertainty. In this paper, the classic operator norm approach is augmented by linearising the stochastic FE model around the mean of the aleatory uncertain parameters. This allows for determining those values of the epistemically uncertain parameters that yield an extremum in the failure probability without solving the associated reliability problem. Hence, the double loop that is typically associated to this type of problems is effectively broken. A case study illustrates the effectiveness and efficiency of the proposed method.
Keywords
- Gaussian loading, Interval failure probability, Interval variables, Linear structural system
ASJC Scopus subject areas
- Decision Sciences(all)
- Management Science and Operations Research
- Engineering(all)
- Safety, Risk, Reliability and Quality
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Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. ed. / Michael Beer; Enrico Zio; Kok-Kwang Phoon; Bilal M. Ayyub. 2022. p. 498-501 (Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Bounding Failure Probabilities in Imprecise Stochastic FE models
AU - Faes, Matthias G.R.
AU - Fina, Marc
AU - Valdebenito, Marcos A.
AU - Lauff, Celine
AU - Wagner, Werner
AU - Freitag, Steffen
AU - Beer, Michael
PY - 2022
Y1 - 2022
N2 - This paper presents a highly efficient and effective approach to bound the first excursion probability of linear stochastic FE models subjected to imprecise Gaussian excitations. In previous work, some of the authors proposed a highly efficient approach based on the operator norm framework to bound such first excursion probabilities without having to resort to double-loop problems [1]. However very efficient, the approach presented in [1] is limited to deterministic models, or models containing epistemic uncertainty. In this paper, the classic operator norm approach is augmented by linearising the stochastic FE model around the mean of the aleatory uncertain parameters. This allows for determining those values of the epistemically uncertain parameters that yield an extremum in the failure probability without solving the associated reliability problem. Hence, the double loop that is typically associated to this type of problems is effectively broken. A case study illustrates the effectiveness and efficiency of the proposed method.
AB - This paper presents a highly efficient and effective approach to bound the first excursion probability of linear stochastic FE models subjected to imprecise Gaussian excitations. In previous work, some of the authors proposed a highly efficient approach based on the operator norm framework to bound such first excursion probabilities without having to resort to double-loop problems [1]. However very efficient, the approach presented in [1] is limited to deterministic models, or models containing epistemic uncertainty. In this paper, the classic operator norm approach is augmented by linearising the stochastic FE model around the mean of the aleatory uncertain parameters. This allows for determining those values of the epistemically uncertain parameters that yield an extremum in the failure probability without solving the associated reliability problem. Hence, the double loop that is typically associated to this type of problems is effectively broken. A case study illustrates the effectiveness and efficiency of the proposed method.
KW - Gaussian loading
KW - Interval failure probability
KW - Interval variables
KW - Linear structural system
UR - http://www.scopus.com/inward/record.url?scp=85192742116&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_MS-15-141-cd
DO - 10.3850/978-981-18-5184-1_MS-15-141-cd
M3 - Conference contribution
AN - SCOPUS:85192742116
SN - 9789811851841
T3 - Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
SP - 498
EP - 501
BT - Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
A2 - Beer, Michael
A2 - Zio, Enrico
A2 - Phoon, Kok-Kwang
A2 - Ayyub, Bilal M.
T2 - 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
Y2 - 4 September 2022 through 7 September 2022
ER -