A dual adaptive filtering approach for nonlinear finite element model updating accounting for modeling uncertainty. (15th January 2019) - Astroza, Rodrigo; Alessandri, Andrés; Conte, Joel P.
Highlights: Recursive Bayesian updating of mechanics-based nonlinear FE models is performed. A novel approach to account for modeling uncertainty is proposed. A realistic 2D building subjected to seismic excitation is studied. Different types and levels of modeling uncertainty are investigated. The robustness of the proposed approach is demonstrated. Abstract: 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.