A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.
|Title of host publication||Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019|
|Publisher||Springer New York LLC|
|Number of pages||11|
|State||Published - 2020|
|Event||37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 - Orlando, United States|
Duration: 28 Jan 2019 → 31 Jan 2019
|Name||Conference Proceedings of the Society for Experimental Mechanics Series|
|Conference||37th IMAC, A Conference and Exposition on Structural Dynamics, 2019|
|Period||28/01/19 → 31/01/19|
Bibliographical noteFunding Information:
R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009.
Acknowledgements R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009.
© Society for Experimental Mechanics, Inc. 2020.
- Dual filtering
- Finite element model
- Modeling uncertainty
- Parameter estimation