This paper proposes a novel approach to deal with modeling uncertainty when updating mechanics-based nonlinear finite element (FE) models. In this framework, 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 unidirectional horizontal seismic excitation is used to illustrate and validate the proposed methodology. Geometry, inertia properties, gravity loads, and damping properties are considered as sources of modeling uncertainty and different levels and combinations of them are analyzed. 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. Thus, a more robust and comprehensive identification of structural damage is achieved when using the proposed approach. A different input motion is then considered to verify the prediction capabilities of the proposed methodology by using the FE model updated by the parameter estimation results obtained.
|Number of pages||19|
|Journal||Mechanical Systems and Signal Processing|
|State||Published - 15 Jan 2019|
Bibliographical noteFunding Information:
R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), FONDECYT project No. 11160009, and from the Universidad de los Andes, Chile through the research grant Fondo de Ayuda a la Investigación (FAI).
© 2018 Elsevier Ltd
- Bayesian method
- FE model updating
- Modeling uncertainty
- Nonlinear FE model
- Parameter estimation
- Structural health monitoring