System identification has become an important field due to the growing need to estimate the behavior of systems with a partially known dynamic. In recent decades, many optimization techniques have been developed for system identification problems. Identification is essentially a process of developing or improving mathematical models of dynamic systems using measured experimental data. However, when performing system identification analyzes, strong motion data collection is essential. In addition to updating the structural parameters to better predict the response, system identification techniques have enabled monitoring of the current state or damage state of the structure.
The method proposed to the parameter identification in this study is the GA-based extended Kalman filter. For the GA-based extended Kalman filter, the Wen’s model is used as the restoring force of each story shear and the analysis of the extended Kalman filter is performed after the initial values of the state variables used in the analysis are acquired by the evolution process of the GA for each generation. The process of exploring this algorithm is implemented using the simulated SDOF system and the MDOF system considering the effect of noise contamination. Finally, th identification strategy is also applied to the identification of the three-story steel frame with added-damping-and-stiffness devices.
According to the results of GA-based extended Kalman filter, the conclusions are as follows: (1) The first DOF stiffness will decrease as the PGA value increases. (2) The behavior at higher DOF tends to be linear when the same excitation intensity is applied. (3) The D20H80 model of added-damping-and-stiffness devices suffered extensive damage when subjected to an El 600 earthquake.