Finite element model updating accounting for modeling uncertainty

Rodrigo Astroza, Andres Alessandri, Joel P. Conte

Research output: Contribution to conferencePaper

Abstract

2020. 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.
Original languageAmerican English
Pages211-221
Number of pages11
DOIs
StatePublished - 1 Jan 2020
EventConference Proceedings of the Society for Experimental Mechanics Series -
Duration: 1 Jan 2020 → …

Conference

ConferenceConference Proceedings of the Society for Experimental Mechanics Series
Period1/01/20 → …

Keywords

  • Dual filtering
  • Finite element model
  • Modeling uncertainty
  • Parameter estimation

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