Details
| Original language | English |
|---|---|
| Article number | 111078 |
| Number of pages | 15 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 208 |
| Early online date | 30 Dec 2023 |
| Publication status | Published - 15 Feb 2024 |
Abstract
In stochastic dynamics, ensuring the structural reliability of buildings and structures is of paramount importance, especially when subjected to environmental loads such as wind or earthquakes. To adequately address these loads and the uncertainties associated with them, it is often necessary to utilise advanced load models, frequently expressed using a power spectral density (PSD) function. The construction of these load models becomes challenging when only limited data is available and meaningful statistics cannot be reliably derived. To address this issue, safety bounds are commonly used in load models to account for uncertainties. Many PSD functions, such as the Clough–Penzien model, are described by parameters with a physical background and can therefore reflect the real case. The aim of this work is to expand these physical parameters in order to account for uncertainties. For this purpose, bootstrapping is used to derive more reliable statistics. By introducing a scaling parameter that allows for flexibility, bounds of the data set can be derived. Consequently, suitable PSD models are fitted to the derived bounds. The PSD function is thus represented by intervals for its physical properties instead of relying on discrete values. When applying such a bounded load model to a structure, advanced interval propagation schemes can be utilised to bound the failure probability.
Keywords
- Power spectral density function, Random vibrations, Stochastic dynamics, Stochastic processes, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 208, 111078, 15.02.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Data-driven and physics-based interval modelling of power spectral density functions from limited data
AU - Behrendt, Marco
AU - Dang, Chao
AU - Beer, Michael
N1 - Funding Information: Chao Dang would like to thank the support of the China Scholarship Council (CSC) .
PY - 2024/2/15
Y1 - 2024/2/15
N2 - In stochastic dynamics, ensuring the structural reliability of buildings and structures is of paramount importance, especially when subjected to environmental loads such as wind or earthquakes. To adequately address these loads and the uncertainties associated with them, it is often necessary to utilise advanced load models, frequently expressed using a power spectral density (PSD) function. The construction of these load models becomes challenging when only limited data is available and meaningful statistics cannot be reliably derived. To address this issue, safety bounds are commonly used in load models to account for uncertainties. Many PSD functions, such as the Clough–Penzien model, are described by parameters with a physical background and can therefore reflect the real case. The aim of this work is to expand these physical parameters in order to account for uncertainties. For this purpose, bootstrapping is used to derive more reliable statistics. By introducing a scaling parameter that allows for flexibility, bounds of the data set can be derived. Consequently, suitable PSD models are fitted to the derived bounds. The PSD function is thus represented by intervals for its physical properties instead of relying on discrete values. When applying such a bounded load model to a structure, advanced interval propagation schemes can be utilised to bound the failure probability.
AB - In stochastic dynamics, ensuring the structural reliability of buildings and structures is of paramount importance, especially when subjected to environmental loads such as wind or earthquakes. To adequately address these loads and the uncertainties associated with them, it is often necessary to utilise advanced load models, frequently expressed using a power spectral density (PSD) function. The construction of these load models becomes challenging when only limited data is available and meaningful statistics cannot be reliably derived. To address this issue, safety bounds are commonly used in load models to account for uncertainties. Many PSD functions, such as the Clough–Penzien model, are described by parameters with a physical background and can therefore reflect the real case. The aim of this work is to expand these physical parameters in order to account for uncertainties. For this purpose, bootstrapping is used to derive more reliable statistics. By introducing a scaling parameter that allows for flexibility, bounds of the data set can be derived. Consequently, suitable PSD models are fitted to the derived bounds. The PSD function is thus represented by intervals for its physical properties instead of relying on discrete values. When applying such a bounded load model to a structure, advanced interval propagation schemes can be utilised to bound the failure probability.
KW - Power spectral density function
KW - Random vibrations
KW - Stochastic dynamics
KW - Stochastic processes
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85182446818&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2023.111078
DO - 10.1016/j.ymssp.2023.111078
M3 - Article
AN - SCOPUS:85182446818
VL - 208
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 111078
ER -