Abstract
The Bouc–Wen hysteresis model has been extensively used to identify earthquake damage on civil structures where hysteretic behavior is expected. One of the challenging problems in the identification of Bouc–Wen parameters is overfitting, which is caused by too many nonphysical model parameters. Limit on the search domain of parameters helps to solve the problem. The unscented Kalman filter (UKF) is widely developed to find Bouc–Wen parameters. In order to improve the accuracy and convergence speed of conventional UKF, this study developed a constrained UKF (CUKF) which can be used for the simultaneous identification of the Bouc–Wen hysteretic model. The proposed CUKF is very helpful when certain parameters can be constrained in a physical sense. We compare the proposed CUKF with conventional UKF to validate its robustness and efficacy in identifying Bouc–Wen parameters.
Highlights
Over the past few decades, several structural identification techniques have been successfully implemented for high-rise and long-span structures based on the development of computing and measurement technology
We propose a constrained unscented Kalman filter (UKF) (CUKF) that combines a constrained minimization technique with a conventional UKF to determine the appropriate constraints for state and parameter identification. e proposed method is compared with conventional UKF-based parameter identification techniques to validate its robustness and efficacy in identifying Bouc–Wen parameters
E advantage of introducing the constrained minimization technique in the parameter estimation problem is that the predicted parameters are bounded by the reasonable constraints. e constraints would make the UKF estimate more accurate compared with the conventional UKF estimate
Summary
Over the past few decades, several structural identification techniques have been successfully implemented for high-rise and long-span structures based on the development of computing and measurement technology. The Bouc–Wen model has been successfully extended to create several variations, for assessing strength degradation, stiffness degradation [3], pinching effects [4,5,6], and asymmetric behavior [7,8,9,10] It has been widely adopted for the description of hysteretic structures [11,12,13,14]. To estimate the states of the nonlinear structures accurately, various numerical techniques have been investigated including the extended Kalman filter (EKF) [15,16,17,18], unscented Kalman filter (UKF) [19,20,21,22,23,24,25,26], and particle filter [27]. We propose a constrained UKF (CUKF) that combines a constrained minimization technique with a conventional UKF to determine the appropriate constraints for state and parameter identification. e proposed method is compared with conventional UKF-based parameter identification techniques to validate its robustness and efficacy in identifying Bouc–Wen parameters
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