Details
| Original language | English |
|---|---|
| Number of pages | 6 |
| Publication status | Published - 26 May 2019 |
| Event | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 - Seoul, South Korea, Seoul, Korea, Republic of Duration: 26 May 2019 → 30 May 2019 Conference number: 13 |
Conference
| Conference | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 |
|---|---|
| Abbreviated title | ICASP13 |
| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 26 May 2019 → 30 May 2019 |
Abstract
Modern approaches to solve dynamic problems where random vibration is of significance will in most of cases rely upon the fundamental concept of the power spectrum as a core model for excitation and response process representation. This is partly due to the practicality of spectral models for frequency domain analysis, as well as their ease of use for generating compatible time domain samples. Such samples may be utilised for numerical performance evaluation of structures, those represented by complex non-linear models. Utilisation of ensemble statistics will be considered first for stationary processes only. For a stationary stochastic process, its power spectrum can be estimated statistically across all time or for a single window in time across an ensemble of records. In this work, it is first shown that ensemble characteristics can be utilised to improve the resulting power spectra by using estimations of the median instead of the mean of multiple data records. The improved power spectrum will be more robust in the presence of spectral outliers. The median spectrum will result in more reliable response statistics, particularly when source ensemble records contain low power spectra that are significantly below the mean. A weighted median spectrum will also be utilised, based upon the spectral distance of each record from the median, which will shift the estimated spectrum in the direction of the closest samples. In some cases, the data records exhibit high spectral variance so such an extent that a single power spectrum estimate is insufficient to adequately model the process statistics. In such cases, a more realistic representation of the spectral range of the process is captured by estimating two or more power spectra. This is done by classifying individual process records based upon their individual spectral estimates' distance from each other, and therefore the only parameterisation required is to choose the number of spectrum models to be defined.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Mathematics(all)
- Statistics and Probability
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2019. Paper presented at 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019, Seoul, Korea, Republic of.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Stochastic processes identification from data ensembles via power spectrum classification
AU - Behrendt, Marco
AU - Comerford, Liam
AU - Beer, Michael
N1 - Conference code: 13
PY - 2019/5/26
Y1 - 2019/5/26
N2 - Modern approaches to solve dynamic problems where random vibration is of significance will in most of cases rely upon the fundamental concept of the power spectrum as a core model for excitation and response process representation. This is partly due to the practicality of spectral models for frequency domain analysis, as well as their ease of use for generating compatible time domain samples. Such samples may be utilised for numerical performance evaluation of structures, those represented by complex non-linear models. Utilisation of ensemble statistics will be considered first for stationary processes only. For a stationary stochastic process, its power spectrum can be estimated statistically across all time or for a single window in time across an ensemble of records. In this work, it is first shown that ensemble characteristics can be utilised to improve the resulting power spectra by using estimations of the median instead of the mean of multiple data records. The improved power spectrum will be more robust in the presence of spectral outliers. The median spectrum will result in more reliable response statistics, particularly when source ensemble records contain low power spectra that are significantly below the mean. A weighted median spectrum will also be utilised, based upon the spectral distance of each record from the median, which will shift the estimated spectrum in the direction of the closest samples. In some cases, the data records exhibit high spectral variance so such an extent that a single power spectrum estimate is insufficient to adequately model the process statistics. In such cases, a more realistic representation of the spectral range of the process is captured by estimating two or more power spectra. This is done by classifying individual process records based upon their individual spectral estimates' distance from each other, and therefore the only parameterisation required is to choose the number of spectrum models to be defined.
AB - Modern approaches to solve dynamic problems where random vibration is of significance will in most of cases rely upon the fundamental concept of the power spectrum as a core model for excitation and response process representation. This is partly due to the practicality of spectral models for frequency domain analysis, as well as their ease of use for generating compatible time domain samples. Such samples may be utilised for numerical performance evaluation of structures, those represented by complex non-linear models. Utilisation of ensemble statistics will be considered first for stationary processes only. For a stationary stochastic process, its power spectrum can be estimated statistically across all time or for a single window in time across an ensemble of records. In this work, it is first shown that ensemble characteristics can be utilised to improve the resulting power spectra by using estimations of the median instead of the mean of multiple data records. The improved power spectrum will be more robust in the presence of spectral outliers. The median spectrum will result in more reliable response statistics, particularly when source ensemble records contain low power spectra that are significantly below the mean. A weighted median spectrum will also be utilised, based upon the spectral distance of each record from the median, which will shift the estimated spectrum in the direction of the closest samples. In some cases, the data records exhibit high spectral variance so such an extent that a single power spectrum estimate is insufficient to adequately model the process statistics. In such cases, a more realistic representation of the spectral range of the process is captured by estimating two or more power spectra. This is done by classifying individual process records based upon their individual spectral estimates' distance from each other, and therefore the only parameterisation required is to choose the number of spectrum models to be defined.
UR - http://www.scopus.com/inward/record.url?scp=85083951182&partnerID=8YFLogxK
U2 - 10.22725/ICASP13.407
DO - 10.22725/ICASP13.407
M3 - Paper
T2 - 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019
Y2 - 26 May 2019 through 30 May 2019
ER -