Abstract
This paper introduces a new approach for model calibration based on fidelity maps. Fidelity maps refer to the regions of the parameter space within which the discrepancy between computational and experimental data is below a user-defined threshold. It is shown that fidelity maps, which are built explicilty in terms of the calibration parameters and aleatory variables, provide a rigourous approximation of the likelihood for maximum likelihood estimation or Bayesian update. Because the maps are constructed using a support vector machine classifier (SVM), the approach has the advantage of handling numerous correlated responses, possibly discontinuous, at a moderate computational cost.This is made possible by the use of a dedicated adaptive sampling scheme to refine the SVM classifier. A simply supported plate with uncertainties in the boundary conditions is used to demonstrate the methodology. In this example, the construction of the map and the Bayesian calibration is based on several natural frequencies and mode shapes to be matched simultaneously.
| Original language | English (US) |
|---|---|
| Title of host publication | Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 |
| Pages | 3289-3296 |
| Number of pages | 8 |
| State | Published - 2013 |
| Event | 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 - New York, NY, United States Duration: Jun 16 2013 → Jun 20 2013 |
Other
| Other | 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 6/16/13 → 6/20/13 |
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ASJC Scopus subject areas
- Civil and Structural Engineering
- Safety, Risk, Reliability and Quality
Cite this
Bayesian calibration using fidelity maps. / Lacaze, Sylvain; Missoum, Samy.
Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013. 2013. p. 3289-3296.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Bayesian calibration using fidelity maps
AU - Lacaze, Sylvain
AU - Missoum, Samy
PY - 2013
Y1 - 2013
N2 - This paper introduces a new approach for model calibration based on fidelity maps. Fidelity maps refer to the regions of the parameter space within which the discrepancy between computational and experimental data is below a user-defined threshold. It is shown that fidelity maps, which are built explicilty in terms of the calibration parameters and aleatory variables, provide a rigourous approximation of the likelihood for maximum likelihood estimation or Bayesian update. Because the maps are constructed using a support vector machine classifier (SVM), the approach has the advantage of handling numerous correlated responses, possibly discontinuous, at a moderate computational cost.This is made possible by the use of a dedicated adaptive sampling scheme to refine the SVM classifier. A simply supported plate with uncertainties in the boundary conditions is used to demonstrate the methodology. In this example, the construction of the map and the Bayesian calibration is based on several natural frequencies and mode shapes to be matched simultaneously.
AB - This paper introduces a new approach for model calibration based on fidelity maps. Fidelity maps refer to the regions of the parameter space within which the discrepancy between computational and experimental data is below a user-defined threshold. It is shown that fidelity maps, which are built explicilty in terms of the calibration parameters and aleatory variables, provide a rigourous approximation of the likelihood for maximum likelihood estimation or Bayesian update. Because the maps are constructed using a support vector machine classifier (SVM), the approach has the advantage of handling numerous correlated responses, possibly discontinuous, at a moderate computational cost.This is made possible by the use of a dedicated adaptive sampling scheme to refine the SVM classifier. A simply supported plate with uncertainties in the boundary conditions is used to demonstrate the methodology. In this example, the construction of the map and the Bayesian calibration is based on several natural frequencies and mode shapes to be matched simultaneously.
UR - http://www.scopus.com/inward/record.url?scp=84892401526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84892401526&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84892401526
SN - 9781138000865
SP - 3289
EP - 3296
BT - Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
ER -