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Bayesian based nonlinear model updating using instantaneous characteristics of structural dynamic responses
https://orcid.org/0000-0002-0148-0419
https://orcid.org/0000-0003-0314-0407
Highlights A Bayesian based nonlinear model updating approach is proposed. Instantaneous amplitudes of the decomposed dynamic responses are used. Uncertainty quantification of the model updating results due to noise is conducted. Numerical and experimental investigations are conducted to validate the approach. Reliable and accurate nonlinear model updating results are obtained.
Abstract This paper proposes a Bayesian based nonlinear model updating approach using the instantaneous amplitudes of the decomposed dynamic responses. Uncertainty quantification of the model updating results due to the measurement noise is conducted. The residual of the instantaneous amplitudes of the decomposed structural dynamic responses between the test structure and the analytical nonlinear model is used to construct the maximum likelihood function. Since nonlinear model parameters and simulated error variances of the instantaneous parameters are all unknown, the extended maximum likelihood estimation method is used to update these parameters. The uncertainty in the updated nonlinear model parameters can be evaluated by using the Cram-Rao lower bound theorem with the exact Fisher Information matrix. A numerical study on a three-storey building structure model under earthquake excitation is performed to verify the accuracy and performance of the proposed approach. An experimental verification on a high voltage switch structure under harmonic excitation is conducted to investigate the accuracy of using the proposed approach for nonlinear model updating. Both numerical and experimental results demonstrate that the proposed approach is reliable and accurate for nonlinear model updating, with the capacity of considering the uncertain noise effect in the measurements.
Bayesian based nonlinear model updating using instantaneous characteristics of structural dynamic responses
https://orcid.org/0000-0002-0148-0419
https://orcid.org/0000-0003-0314-0407
Highlights A Bayesian based nonlinear model updating approach is proposed. Instantaneous amplitudes of the decomposed dynamic responses are used. Uncertainty quantification of the model updating results due to noise is conducted. Numerical and experimental investigations are conducted to validate the approach. Reliable and accurate nonlinear model updating results are obtained.
Abstract This paper proposes a Bayesian based nonlinear model updating approach using the instantaneous amplitudes of the decomposed dynamic responses. Uncertainty quantification of the model updating results due to the measurement noise is conducted. The residual of the instantaneous amplitudes of the decomposed structural dynamic responses between the test structure and the analytical nonlinear model is used to construct the maximum likelihood function. Since nonlinear model parameters and simulated error variances of the instantaneous parameters are all unknown, the extended maximum likelihood estimation method is used to update these parameters. The uncertainty in the updated nonlinear model parameters can be evaluated by using the Cram-Rao lower bound theorem with the exact Fisher Information matrix. A numerical study on a three-storey building structure model under earthquake excitation is performed to verify the accuracy and performance of the proposed approach. An experimental verification on a high voltage switch structure under harmonic excitation is conducted to investigate the accuracy of using the proposed approach for nonlinear model updating. Both numerical and experimental results demonstrate that the proposed approach is reliable and accurate for nonlinear model updating, with the capacity of considering the uncertain noise effect in the measurements.
Bayesian based nonlinear model updating using instantaneous characteristics of structural dynamic responses
https://orcid.org/0000-0002-0148-0419
https://orcid.org/0000-0003-0314-0407
Highlights A Bayesian based nonlinear model updating approach is proposed. Instantaneous amplitudes of the decomposed dynamic responses are used. Uncertainty quantification of the model updating results due to noise is conducted. Numerical and experimental investigations are conducted to validate the approach. Reliable and accurate nonlinear model updating results are obtained.
Abstract This paper proposes a Bayesian based nonlinear model updating approach using the instantaneous amplitudes of the decomposed dynamic responses. Uncertainty quantification of the model updating results due to the measurement noise is conducted. The residual of the instantaneous amplitudes of the decomposed structural dynamic responses between the test structure and the analytical nonlinear model is used to construct the maximum likelihood function. Since nonlinear model parameters and simulated error variances of the instantaneous parameters are all unknown, the extended maximum likelihood estimation method is used to update these parameters. The uncertainty in the updated nonlinear model parameters can be evaluated by using the Cram-Rao lower bound theorem with the exact Fisher Information matrix. A numerical study on a three-storey building structure model under earthquake excitation is performed to verify the accuracy and performance of the proposed approach. An experimental verification on a high voltage switch structure under harmonic excitation is conducted to investigate the accuracy of using the proposed approach for nonlinear model updating. Both numerical and experimental results demonstrate that the proposed approach is reliable and accurate for nonlinear model updating, with the capacity of considering the uncertain noise effect in the measurements.
Bayesian based nonlinear model updating using instantaneous characteristics of structural dynamic responses
Xin, Yu (author) / Hao, Hong (author) / Li, Jun (author) / Wang, Zuo-Cai (author) / Wan, Hua-Ping (author) / Ren, Wei-Xin (author)
Engineering Structures ; 183 ; 459-474
2019-01-09
16 pages
Article (Journal)
Electronic Resource
English
Bayesian Model Updating of a Simply-Supported Truss Bridge Based on Dynamic Responses
| Springer Verlag | 2022