Performance comparison of Kalman−based filters for nonlinear structural finite element model updating

Rodrigo Astroza, Hamed Ebrahimian, Joel P. Conte

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Finite element (FE) model updating has emerged as a powerful technique for structural health monitoring and damage identification of civil structures. Updating mechanics-based nonlinear FE models allows for a complete and comprehensive damage diagnosis of large and complex structures, but it is computationally demanding. This paper first introduces an Iterated Extended Kalman filter (IEKF) to update mechanics-based nonlinear FE models of civil structures. Different model updating techniques using the Extended Kalman filter (EKF), Unscented Kalman Filter (UKF) and IEKF, are then compared for their performance in terms of convergence, accuracy, robustness, and computational demand. Finally, a non-recursive estimation procedure is presented and its effectiveness in reducing the computational cost, while maintaining accuracy and robustness, is demonstrated. An application example is presented based on numerically simulated response data for a three-dimensional 5-story 2-by-1 bay reinforced concrete (RC) frame building subjected to bi-directional earthquake excitation. Excellent estimation results are obtained with the EKF, UKF, and IEKF used in conjunction with the proposed non-recursive estimation approach. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the model parameters are observed with these filters, which may lead to divergence of the nonlinear FE model solution procedure. The UKF slightly outperforms the EKF and IEKF, but at a higher computational cost.

Original languageEnglish
Pages (from-to)520-542
Number of pages23
JournalJournal of Sound and Vibration
Volume438
DOIs
StatePublished - 6 Jan 2019

Bibliographical note

Funding Information:
Partial support of this research by the UC San Diego Academic Senate under Research Grant RN091G−CONTE is gratefully acknowledged. Rodrigo Astroza acknowledges the financial support of the Universidad de los Andes ̶ Chile through the research grant Fondo de Ayuda a la Investigación (FAI), and from the ( Chilean National Commission for Scientific and Technological Research CONICYT ), through FONDECYT-Iniciación research grant No. 11160009 . Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsors.

Funding Information:
Partial support of this research by the UC San Diego Academic Senate under Research Grant RN091G−CONTE is gratefully acknowledged. Rodrigo Astroza acknowledges the financial support of the Universidad de los Andes ̶ Chile through the research grant Fondo de Ayuda a la Investigación (FAI), and from the (Chilean National Commission for Scientific and Technological Research CONICYT), through FONDECYT-Iniciación research grant No. 11160009. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsors.

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • Damage identification
  • Kalman-based filter
  • Nonlinear finite element model
  • Parameter estimation

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