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Smart Structures and Systems Volume 26, Number 6, December 2020 , pages 693-701 DOI: https://doi.org/10.12989/sss.2020.26.6.693 |
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A two-stage Kalman filter for the identification of structural parameters with unknown loads |
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Jia He, Xiaoxiong Zhang, Zhouquan Feng, Zhengqing Chen and Zhang Cao
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| Abstract | ||
| The conventional Kalman Filter (KF) provides a promising way for structural state estimation. However, the physical parameters of structural systems or models should be available for the estimation. Moreover, it is not applicable when the loadings applied to the structures are unknown. To circumvent the aforementioned limitations, a two-stage KF with unknown input approach is proposed for the simultaneous identification of structural parameters and unknown loadings. In stage 1, a modified observation equation is employed. The structural state vector is estimated by KF on the basis of structural parameters identified at the previous time-step. Then, the unknown input is identified by Least Squares Estimation (LSE). In stage 2, based on the concept of sensitivity matrix, the structural parameters are updated at the current time-step by using the estimated structural states obtained from stage 1. The effectiveness of the proposed approach is numerically validated via a five-story shearing model under random and earthquake excitations. Shaking table tests on a five-story structure are also employed to demonstrate the performance of the proposed approach. It is demonstrated from numerical and experimental results that the proposed approach can be used for the identification of parameters of structure and the external force applied to it with acceptable accuracy. | ||
| Key Words | ||
| two-stage Kalman filter; parameter identification; unknown loading; modified observation equation; sensitivity matrix | ||
| Address | ||
| College of Civil Engineering, Key laboratory of wind and bridge engineering of Hunan Province, Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, China. | ||