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 | 793-799 |
| Seitenumfang | 7 |
| Publikationsstatus | Veröffentlicht - Sept. 2022 |
Abstract
We address a novel probability distribution, namely the log-Rayleigh distribution, suited to model the maximum occurring loads in a log-normal process. Log-normal processes in engineering applications can be used to model for example wave or wind loads acting on structures. Usually, to assess probabilistic events, e.g. the probability of failure of structures under log-normal load processes, the generation of time-histories is necessary. With the given probability distribution, the maximum load events can directly be sampled, eliminating this step. Also, since a closed form of the PDF is given, the integrals involved in reliability analysis can directly be evaluated. We show that the proposed log-Rayleigh distribution can accurately model the distribution of local maxima in each log-normal process when compared to samples obtained from a Monte Carlo approach. Furthermore, we conduct a parameter study to evaluate the influence of the parameters in the log-Rayleigh distribution. Details on the generation of a log-normal process and a benchmark of this process are also included. Finally, a mechanical model related to a static structural reliability analysis is evaluated to show suitable utilities of the newly formed log-Rayleigh distribution.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
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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. 793-799.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - The Log-Rayleigh Distribution for Local Maxima of spectrally Represented Log-normal Processes
AU - Grashorn, Jan
AU - Bittner, Marius
AU - Wang, Cao
AU - Beer, Michael
N1 - Publisher Copyright: © 2022 ISRERM Organizers. Published by Research Publishing, Singapore.
PY - 2022/9
Y1 - 2022/9
N2 - We address a novel probability distribution, namely the log-Rayleigh distribution, suited to model the maximum occurring loads in a log-normal process. Log-normal processes in engineering applications can be used to model for example wave or wind loads acting on structures. Usually, to assess probabilistic events, e.g. the probability of failure of structures under log-normal load processes, the generation of time-histories is necessary. With the given probability distribution, the maximum load events can directly be sampled, eliminating this step. Also, since a closed form of the PDF is given, the integrals involved in reliability analysis can directly be evaluated. We show that the proposed log-Rayleigh distribution can accurately model the distribution of local maxima in each log-normal process when compared to samples obtained from a Monte Carlo approach. Furthermore, we conduct a parameter study to evaluate the influence of the parameters in the log-Rayleigh distribution. Details on the generation of a log-normal process and a benchmark of this process are also included. Finally, a mechanical model related to a static structural reliability analysis is evaluated to show suitable utilities of the newly formed log-Rayleigh distribution.
AB - We address a novel probability distribution, namely the log-Rayleigh distribution, suited to model the maximum occurring loads in a log-normal process. Log-normal processes in engineering applications can be used to model for example wave or wind loads acting on structures. Usually, to assess probabilistic events, e.g. the probability of failure of structures under log-normal load processes, the generation of time-histories is necessary. With the given probability distribution, the maximum load events can directly be sampled, eliminating this step. Also, since a closed form of the PDF is given, the integrals involved in reliability analysis can directly be evaluated. We show that the proposed log-Rayleigh distribution can accurately model the distribution of local maxima in each log-normal process when compared to samples obtained from a Monte Carlo approach. Furthermore, we conduct a parameter study to evaluate the influence of the parameters in the log-Rayleigh distribution. Details on the generation of a log-normal process and a benchmark of this process are also included. Finally, a mechanical model related to a static structural reliability analysis is evaluated to show suitable utilities of the newly formed log-Rayleigh distribution.
KW - Local maxima
KW - Log-Rayleigh distribution
KW - Probability distribution
KW - Spectral representation
KW - log-normal process
UR - http://www.scopus.com/inward/record.url?scp=85202021734&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_GS-05-108-cd
DO - 10.3850/978-981-18-5184-1_GS-05-108-cd
M3 - Conference contribution
SN - 9789811851841
SP - 793
EP - 799
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.
ER -