Details
| Original language | English |
|---|---|
| Title of host publication | EURODYN 2020 |
| Subtitle of host publication | XI International Conference on Structural Dynamics, Proceedings, Volume II |
| Editors | Manolis Papadrakakis, Michalis Fragiadakis, Costas Papadimitriou |
| Pages | 3803-3815 |
| Number of pages | 13 |
| Volume | 2 |
| Edition | First Edition |
| ISBN (electronic) | 9786188507210 |
| Publication status | Published - 2020 |
| Event | 11th International Conference on Structural Dynamics, EURODYN 2020 - online, Virtual, Athens, Greece Duration: 23 Nov 2020 → 26 Nov 2020 Conference number: 11 https://generalconferencefiles.s3-eu-west-1.amazonaws.com/eurodyn_2020_ebook_procedings_vol2.pdf |
Abstract
In structural dynamics the considerations of statistical uncertainties are imperative to ensure a realistic modelling of loading and material parameter setup. It is well known that any deterministic analysis only constitutes a narrow result for the given input parameters. Because of aleatoric or epistemic uncertainties, many factors must be considered either in certain interval margins or with subjective probabilities. Especially in the case of seismic ground motion, due to significant uncertainties, a reliable prediction of future event characteristics is important in designing safe structures. This work attends to the well known statistical procedure of simulating time histories of a mechanical model under an artificially generated earthquake loading, which is modelled by a stochastic process. The stochastic processes in this work are throughout synthesized using the Spectral Representation Method (SRM). A key aspect of this procedure is the estimation of the Power Spectrum Density (PSD). The PSD determines dominant frequencies and their magnitude of influence on the stochastic process and in nature on the earthquake signals. There are numerous methods to estimate the PSD from source data, however, the amount of data available is seldom enough to do this accurately and reliably. To address this issue, the authors suggest that the PSD model itself could be defined as a random vector in the frequency domain, thereby encompassing a range of possible valid PSD models for a given data set. For a stationary process, the random dimension of said model would be dependent upon its frequency discretisation. On a simple mechanical system, artificially generated stochastic processes with the novel described underlying power spectra are analysed utilizing a Monte Carlo simulation. A reliability statement of the mechanical system in the form of a first-passage problem is defined to acquire a probability of failure which is used alongside first- and second-order moments of the system's output as reasonable benchmark values.
Keywords
- Power Spectral Density, Spectral Representation, Stochastic Dynamics, Stochastic Process, Uncertainty Quantification
ASJC Scopus subject areas
- Engineering(all)
- Building and Construction
- Engineering(all)
- Architecture
- Engineering(all)
- Civil and Structural Engineering
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EURODYN 2020: XI International Conference on Structural Dynamics, Proceedings, Volume II. ed. / Manolis Papadrakakis; Michalis Fragiadakis; Costas Papadimitriou. Vol. 2 First Edition. ed. 2020. p. 3803-3815.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
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TY - GEN
T1 - Parameter investigation of relaxed uncertain power spectra for stochastic dynamic systems
AU - Behrendt, Marco
AU - Bittner, Marius
AU - Comerford, Liam
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Conference code: 11
PY - 2020
Y1 - 2020
N2 - In structural dynamics the considerations of statistical uncertainties are imperative to ensure a realistic modelling of loading and material parameter setup. It is well known that any deterministic analysis only constitutes a narrow result for the given input parameters. Because of aleatoric or epistemic uncertainties, many factors must be considered either in certain interval margins or with subjective probabilities. Especially in the case of seismic ground motion, due to significant uncertainties, a reliable prediction of future event characteristics is important in designing safe structures. This work attends to the well known statistical procedure of simulating time histories of a mechanical model under an artificially generated earthquake loading, which is modelled by a stochastic process. The stochastic processes in this work are throughout synthesized using the Spectral Representation Method (SRM). A key aspect of this procedure is the estimation of the Power Spectrum Density (PSD). The PSD determines dominant frequencies and their magnitude of influence on the stochastic process and in nature on the earthquake signals. There are numerous methods to estimate the PSD from source data, however, the amount of data available is seldom enough to do this accurately and reliably. To address this issue, the authors suggest that the PSD model itself could be defined as a random vector in the frequency domain, thereby encompassing a range of possible valid PSD models for a given data set. For a stationary process, the random dimension of said model would be dependent upon its frequency discretisation. On a simple mechanical system, artificially generated stochastic processes with the novel described underlying power spectra are analysed utilizing a Monte Carlo simulation. A reliability statement of the mechanical system in the form of a first-passage problem is defined to acquire a probability of failure which is used alongside first- and second-order moments of the system's output as reasonable benchmark values.
AB - In structural dynamics the considerations of statistical uncertainties are imperative to ensure a realistic modelling of loading and material parameter setup. It is well known that any deterministic analysis only constitutes a narrow result for the given input parameters. Because of aleatoric or epistemic uncertainties, many factors must be considered either in certain interval margins or with subjective probabilities. Especially in the case of seismic ground motion, due to significant uncertainties, a reliable prediction of future event characteristics is important in designing safe structures. This work attends to the well known statistical procedure of simulating time histories of a mechanical model under an artificially generated earthquake loading, which is modelled by a stochastic process. The stochastic processes in this work are throughout synthesized using the Spectral Representation Method (SRM). A key aspect of this procedure is the estimation of the Power Spectrum Density (PSD). The PSD determines dominant frequencies and their magnitude of influence on the stochastic process and in nature on the earthquake signals. There are numerous methods to estimate the PSD from source data, however, the amount of data available is seldom enough to do this accurately and reliably. To address this issue, the authors suggest that the PSD model itself could be defined as a random vector in the frequency domain, thereby encompassing a range of possible valid PSD models for a given data set. For a stationary process, the random dimension of said model would be dependent upon its frequency discretisation. On a simple mechanical system, artificially generated stochastic processes with the novel described underlying power spectra are analysed utilizing a Monte Carlo simulation. A reliability statement of the mechanical system in the form of a first-passage problem is defined to acquire a probability of failure which is used alongside first- and second-order moments of the system's output as reasonable benchmark values.
KW - Power Spectral Density
KW - Spectral Representation
KW - Stochastic Dynamics
KW - Stochastic Process
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=85098704404&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85098704404
VL - 2
SP - 3803
EP - 3815
BT - EURODYN 2020
A2 - Papadrakakis, Manolis
A2 - Fragiadakis, Michalis
A2 - Papadimitriou, Costas
T2 - 11th International Conference on Structural Dynamics, EURODYN 2020
Y2 - 23 November 2020 through 26 November 2020
ER -