Details
| Original language | English |
|---|---|
| Title of host publication | 16th International Probabilistic Safety Assessment and Management Conference 2022 (PSAM 16) |
| Number of pages | 9 |
| Publication status | Published - 2022 |
| Event | 16th International Conference on Probabilistic Safety Assessment and Management, PSAM 2022 - Honolulu, United States Duration: 26 Jun 2022 → 1 Jul 2022 |
Abstract
In stochastic dynamics, it is indispensable to model environmental processes in order to design structures safely or to determine the reliability of existing structures. Wind loads or earthquakes are examples of these environmental processes and may be described by stochastic processes. Such a process can be characterised by means of the power spectral density (PSD) function in the frequency domain. Based on the PSD function, governing frequencies and their amplitudes can be determined. For the reliable generation of such a load model described by a PSD function, uncertainties that occur in time signals must be taken into account. In this paper, an approach is presented to derive an imprecise PSD model from a limited amount of data. The spectral densities at each frequency are described by reliable bounds instead of relying on discrete values. The advantages of the imprecise PSD model are illustrated and validated with numerical examples in the field of stochastic dynamics.
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
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16th International Probabilistic Safety Assessment and Management Conference 2022 (PSAM 16). 2022.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Capturing Epistemic Uncertainties in the Power Spectral Density for Limited Data Sets
AU - Behrendt, Marco
AU - Faes, Matthias G.R.
AU - Valdebenito, Marcos A.
AU - Beer, Michael
N1 - Publisher Copyright: © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - In stochastic dynamics, it is indispensable to model environmental processes in order to design structures safely or to determine the reliability of existing structures. Wind loads or earthquakes are examples of these environmental processes and may be described by stochastic processes. Such a process can be characterised by means of the power spectral density (PSD) function in the frequency domain. Based on the PSD function, governing frequencies and their amplitudes can be determined. For the reliable generation of such a load model described by a PSD function, uncertainties that occur in time signals must be taken into account. In this paper, an approach is presented to derive an imprecise PSD model from a limited amount of data. The spectral densities at each frequency are described by reliable bounds instead of relying on discrete values. The advantages of the imprecise PSD model are illustrated and validated with numerical examples in the field of stochastic dynamics.
AB - In stochastic dynamics, it is indispensable to model environmental processes in order to design structures safely or to determine the reliability of existing structures. Wind loads or earthquakes are examples of these environmental processes and may be described by stochastic processes. Such a process can be characterised by means of the power spectral density (PSD) function in the frequency domain. Based on the PSD function, governing frequencies and their amplitudes can be determined. For the reliable generation of such a load model described by a PSD function, uncertainties that occur in time signals must be taken into account. In this paper, an approach is presented to derive an imprecise PSD model from a limited amount of data. The spectral densities at each frequency are described by reliable bounds instead of relying on discrete values. The advantages of the imprecise PSD model are illustrated and validated with numerical examples in the field of stochastic dynamics.
UR - http://www.scopus.com/inward/record.url?scp=85146241336&partnerID=8YFLogxK
M3 - Conference contribution
SN - 978-1-71386-375-5
BT - 16th International Probabilistic Safety Assessment and Management Conference 2022 (PSAM 16)
T2 - 16th International Conference on Probabilistic Safety Assessment and Management, PSAM 2022
Y2 - 26 June 2022 through 1 July 2022
ER -