Bayesian Methods for Nonlinear System Identification of Civil Structures

Translated title of the contribution: Métodos bayesianos para la identificación de sistemas no lineales de estructuras civiles

Joel P. Conte*, Rodrigo Astroza, Hamed Ebrahimian

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


This paper presents a new framework for the identification of mechanics-based nonlinear finite element (FE) models of civil structures using Bayesian methods. In this approach, recursive Bayesian estimation methods are utilized to update an advanced nonlinear FE model of the structure using the input-output dynamic data recorded during an earthquake event. Capable of capturing the complex damage mechanisms and failure modes of the structural system, the updated nonlinear FE model can be used to evaluate the state of health of the structure after a damage-inducing event. To update the unknown time-invariant parameters of the FE model, three alternative stochastic filtering methods are used: the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the iterated extended Kalman filter (IEKF). For those estimation methods that require the computation of structural FE response sensitivities with respect to the unknown modeling parameters (EKF and IEKF), the accurate and computationally efficient direct differentiation method (DDM) is used. A three-dimensional five-story two-by-one bay reinforced concrete (RC) frame is used to illustrate the performance of the framework and compare the performance of the different filters in terms of convergence, accuracy, and robustness. Excellent estimation results are obtained with the UKF, EKF, and IEKF. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the modeling parameters are observed when using these filters. The UKF slightly outperforms the EKF and IEKF.

Translated title of the contributionMétodos bayesianos para la identificación de sistemas no lineales de estructuras civiles
Original languageEnglish
Article number03002
JournalMATEC Web of Conferences
StatePublished - 19 Oct 2015
Event6th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2015 - Dubendorf, Zurich, Switzerland
Duration: 19 Oct 201521 Oct 2015

Bibliographical note

Publisher Copyright:
© 2015 Owned by the authors, published by EDP Sciences.


  • Bayesian networks
  • Bearings (machine parts)
  • Earthquakes
  • Extended Kalman filters
  • Failure (mechanical)
  • Kalman filters
  • Nonlinear analysis
  • Parameter estimation
  • Reinforced concrete
  • Stochastic models
  • Stochastic systems
  • Structures (built objects)
  • Vibration analysis


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