Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
| Herausgeber/-innen | Michael Beer, Enrico Zio, Kok-Kwang Phoon, Bilal M. Ayyub |
| Seiten | 323-328 |
| Seitenumfang | 6 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 - Hannover, Deutschland Dauer: 4 Sept. 2022 → 7 Sept. 2022 |
Abstract
The power spectral density (PSD) function is a frequently used method in the field of stochastic dynamics to determine the governing frequencies of environmental processes, such as earthquakes or wind loads. The PSD function allows buildings and structures to be examined for stability or is used when planning new buildings. Realistic load models may be generated from real data sets, which can be applied to simulation models. When working with real data records, however, it can be possible that these show a high variance and only few similarities. These differences can often only be detected in the frequency domain, while the data records show high similarities in the time domain. To counteract this problem, this paper proposes a classification approach that determines the spectral similarity of the individual data sets and assigns them to groups using a k-means algorithm. Based on the individual groups, load models can be generated and applied separately to the model to obtain more accurate simulation results. In order to demonstrate the benefits of this classification approach it is applied to numerical examples.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. Hrsg. / Michael Beer; Enrico Zio; Kok-Kwang Phoon; Bilal M. Ayyub. 2022. S. 323-328.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Classification of power spectra from data sets with high spectral variance for reliability analysis of dynamic structures
AU - Behrendt, Marco
AU - Kitahara, Masaru
AU - Kitahara, Takeshi
AU - Comerford, Liam
AU - Beer, Michael
N1 - Publisher Copyright: © 2022 ISRERM Organizers. Published by Research Publishing, Singapore.
PY - 2022
Y1 - 2022
N2 - The power spectral density (PSD) function is a frequently used method in the field of stochastic dynamics to determine the governing frequencies of environmental processes, such as earthquakes or wind loads. The PSD function allows buildings and structures to be examined for stability or is used when planning new buildings. Realistic load models may be generated from real data sets, which can be applied to simulation models. When working with real data records, however, it can be possible that these show a high variance and only few similarities. These differences can often only be detected in the frequency domain, while the data records show high similarities in the time domain. To counteract this problem, this paper proposes a classification approach that determines the spectral similarity of the individual data sets and assigns them to groups using a k-means algorithm. Based on the individual groups, load models can be generated and applied separately to the model to obtain more accurate simulation results. In order to demonstrate the benefits of this classification approach it is applied to numerical examples.
AB - The power spectral density (PSD) function is a frequently used method in the field of stochastic dynamics to determine the governing frequencies of environmental processes, such as earthquakes or wind loads. The PSD function allows buildings and structures to be examined for stability or is used when planning new buildings. Realistic load models may be generated from real data sets, which can be applied to simulation models. When working with real data records, however, it can be possible that these show a high variance and only few similarities. These differences can often only be detected in the frequency domain, while the data records show high similarities in the time domain. To counteract this problem, this paper proposes a classification approach that determines the spectral similarity of the individual data sets and assigns them to groups using a k-means algorithm. Based on the individual groups, load models can be generated and applied separately to the model to obtain more accurate simulation results. In order to demonstrate the benefits of this classification approach it is applied to numerical examples.
KW - Power spectrum estimation
KW - Reliability assessment
KW - Stochastic dynamics
KW - Stochastic processes
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85202066086&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_MS-11-160-cd
DO - 10.3850/978-981-18-5184-1_MS-11-160-cd
M3 - Conference contribution
AN - SCOPUS:85202066086
SN - 9789811851841
SP - 323
EP - 328
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 -