Structural Damage Identification with a Tuning-free Hybrid Extended Kalman Filter

Da Yo Yun, Taehoon Hong, Dong Eun Lee, Hyo Seon Park

Research output: Contribution to journalArticlepeer-review


As a time domain system identification method, the extended Kalman filter (EKF) has been continuously used for structural damage identification. The performance of the EKF varies greatly depending on the selection of the initial parameters’ values and their combinations. In this paper, to improve the convergence performance of the EKF and to overcome the dependence on the setting parameter values, a hybrid extended Kalman filter (HEKF) for structural damage identification is proposed. As significant properties of the EKF, the structural damage identification, global convergence, stability, and robustness of the HEKF are guaranteed by integrating a genetic algorithm and the EKF. The performance of the HEKF in structural damage identification was investigated in experiments with four 3-story steel frame test models, which were designed for four different damage scenarios.

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning, MSIP) (No. 2018R1A5A1025137).

Publisher Copyright:
© 2020 International Association for Bridge and Structural Engineering (IABSE).

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction


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