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 | 52-58 |
| Number of pages | 7 |
| Publication status | Published - Sept 2022 |
Abstract
In order to design and safely construct buildings and structures that are exposed to environmental processes such as earthquakes and wind loads, simulations are essential in advance. Although simulations are an approximation of reality, they are still dominated by uncertainties, that must be taken into account. These uncertainties can arise for various reasons, such as incorrectly recorded data or inaccurate simulation models. One widely used approach for generating and simulating environmental processes is the power spectral density (PSD) function. It establishes a relationship between the time and frequency domains and determines the relevant frequencies and their magnitude of the transformed signals. Since the model of the PSD function provides discrete values of the density for each frequency, the idea arises to model this density uncertain. For this purpose, statistical values are extracted from an ensemble of similar PSD functions that differ slightly in shape and peak frequency, for example. These values are used to derive a relaxed model of a PSD function. By using such a model, the response processes of buildings and structures are also uncertain, resulting in a range of possible values instead of discrete values. However, a disadvantage of this model is a higher number of random variables that can affect the simulation results. In this work, the stochastic harmonic functions (SHF) are used instead of the spectral representation method (SRM). Stochastic processes generated with both SRM and SHF are compared in a benchmark simulation to assess the advantages.
Keywords
- Power spectral density function, Spectral representation, Stochastic dynamics, Stochastic processes, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Decision Sciences(all)
- Management Science and Operations Research
Cite this
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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. 52-58.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Stochastic process generation from relaxed power spectra utilising stochastic harmonic functions
AU - Behrendt, Marco
AU - Bittner, Marius
AU - Beer, Michael
N1 - Publisher Copyright: ©2022 ISRERM Organizers. Published by Research Publishing, Singapore.
PY - 2022/9
Y1 - 2022/9
N2 - In order to design and safely construct buildings and structures that are exposed to environmental processes such as earthquakes and wind loads, simulations are essential in advance. Although simulations are an approximation of reality, they are still dominated by uncertainties, that must be taken into account. These uncertainties can arise for various reasons, such as incorrectly recorded data or inaccurate simulation models. One widely used approach for generating and simulating environmental processes is the power spectral density (PSD) function. It establishes a relationship between the time and frequency domains and determines the relevant frequencies and their magnitude of the transformed signals. Since the model of the PSD function provides discrete values of the density for each frequency, the idea arises to model this density uncertain. For this purpose, statistical values are extracted from an ensemble of similar PSD functions that differ slightly in shape and peak frequency, for example. These values are used to derive a relaxed model of a PSD function. By using such a model, the response processes of buildings and structures are also uncertain, resulting in a range of possible values instead of discrete values. However, a disadvantage of this model is a higher number of random variables that can affect the simulation results. In this work, the stochastic harmonic functions (SHF) are used instead of the spectral representation method (SRM). Stochastic processes generated with both SRM and SHF are compared in a benchmark simulation to assess the advantages.
AB - In order to design and safely construct buildings and structures that are exposed to environmental processes such as earthquakes and wind loads, simulations are essential in advance. Although simulations are an approximation of reality, they are still dominated by uncertainties, that must be taken into account. These uncertainties can arise for various reasons, such as incorrectly recorded data or inaccurate simulation models. One widely used approach for generating and simulating environmental processes is the power spectral density (PSD) function. It establishes a relationship between the time and frequency domains and determines the relevant frequencies and their magnitude of the transformed signals. Since the model of the PSD function provides discrete values of the density for each frequency, the idea arises to model this density uncertain. For this purpose, statistical values are extracted from an ensemble of similar PSD functions that differ slightly in shape and peak frequency, for example. These values are used to derive a relaxed model of a PSD function. By using such a model, the response processes of buildings and structures are also uncertain, resulting in a range of possible values instead of discrete values. However, a disadvantage of this model is a higher number of random variables that can affect the simulation results. In this work, the stochastic harmonic functions (SHF) are used instead of the spectral representation method (SRM). Stochastic processes generated with both SRM and SHF are compared in a benchmark simulation to assess the advantages.
KW - Power spectral density function
KW - Spectral representation
KW - Stochastic dynamics
KW - Stochastic processes
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85202075535&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_MS-01-220-cd
DO - 10.3850/978-981-18-5184-1_MS-01-220-cd
M3 - Conference contribution
SN - 9789811851841
SP - 52
EP - 58
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 -