Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 062003 |
| Seitenumfang | 10 |
| Fachzeitschrift | Journal of Physics: Conference Series |
| Jahrgang | 2647 |
| Ausgabenummer | 6 |
| Frühes Online-Datum | 28 Juni 2024 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 12th International Conference on Structural Dynamics, EURODYN 2023 - Delft, Niederlande Dauer: 2 Juli 2023 → 5 Juli 2023 |
Abstract
Buildings and structures in many regions of the world are exposed to environmental factors that can cause damage or failure, making it essential to model these factors accurately in engineering. Stochastic dynamics are crucial for modelling environmental processes, such as earthquake ground motions and wind loads, which can be characterised by a power spectral density (PSD) function that determines the dominant frequencies and corresponding amplitudes of the process. However, when generating a load model described by a PSD function, uncertainties in the processes must be taken into account, which makes a reliable estimation of the PSD function challenging, especially with only limited data available. This study employs the recently developed imprecise PSD function by using a radial basis function network to optimise data-enclosing bounds that produce an interval-valued PSD function. With this approach, a data set in the frequency domain can be bounded for uncertainty quantification. The method described in this work consists of optimising best-case and worst-case PSD functions within the bounds, which are transformed into a separable evolutionary PSD function. The reliability of structures is determined by an upper and lower failure probability, taking into account the present uncertainties. Advanced interval propagation schemes are linked to the imprecise PSD function to determine the failure probabilities efficiently. The method is illustrated by means of three numerical examples.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Allgemeine Physik und Astronomie
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in: Journal of Physics: Conference Series, Jahrgang 2647, Nr. 6, 062003, 2024.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Structural reliability analysis using imprecise evolutionary power spectral density functions
AU - Behrendt, Marco
AU - Dang, Chao
AU - Faes, Matthias G.R.
AU - Valdebenito, Marcos A.
AU - Beer, Michael
N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.
PY - 2024
Y1 - 2024
N2 - Buildings and structures in many regions of the world are exposed to environmental factors that can cause damage or failure, making it essential to model these factors accurately in engineering. Stochastic dynamics are crucial for modelling environmental processes, such as earthquake ground motions and wind loads, which can be characterised by a power spectral density (PSD) function that determines the dominant frequencies and corresponding amplitudes of the process. However, when generating a load model described by a PSD function, uncertainties in the processes must be taken into account, which makes a reliable estimation of the PSD function challenging, especially with only limited data available. This study employs the recently developed imprecise PSD function by using a radial basis function network to optimise data-enclosing bounds that produce an interval-valued PSD function. With this approach, a data set in the frequency domain can be bounded for uncertainty quantification. The method described in this work consists of optimising best-case and worst-case PSD functions within the bounds, which are transformed into a separable evolutionary PSD function. The reliability of structures is determined by an upper and lower failure probability, taking into account the present uncertainties. Advanced interval propagation schemes are linked to the imprecise PSD function to determine the failure probabilities efficiently. The method is illustrated by means of three numerical examples.
AB - Buildings and structures in many regions of the world are exposed to environmental factors that can cause damage or failure, making it essential to model these factors accurately in engineering. Stochastic dynamics are crucial for modelling environmental processes, such as earthquake ground motions and wind loads, which can be characterised by a power spectral density (PSD) function that determines the dominant frequencies and corresponding amplitudes of the process. However, when generating a load model described by a PSD function, uncertainties in the processes must be taken into account, which makes a reliable estimation of the PSD function challenging, especially with only limited data available. This study employs the recently developed imprecise PSD function by using a radial basis function network to optimise data-enclosing bounds that produce an interval-valued PSD function. With this approach, a data set in the frequency domain can be bounded for uncertainty quantification. The method described in this work consists of optimising best-case and worst-case PSD functions within the bounds, which are transformed into a separable evolutionary PSD function. The reliability of structures is determined by an upper and lower failure probability, taking into account the present uncertainties. Advanced interval propagation schemes are linked to the imprecise PSD function to determine the failure probabilities efficiently. The method is illustrated by means of three numerical examples.
UR - http://www.scopus.com/inward/record.url?scp=85198028736&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2647/6/062003
DO - 10.1088/1742-6596/2647/6/062003
M3 - Conference article
AN - SCOPUS:85198028736
VL - 2647
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 6
M1 - 062003
T2 - 12th International Conference on Structural Dynamics, EURODYN 2023
Y2 - 2 July 2023 through 5 July 2023
ER -