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A platform for research: civil engineering, architecture and urbanism
Data-Driven Nonparametric Structural Nonlinearity Identification Under Unknown Excitation with Limited Data Fusion
In this paper, a nonparametric data-driven NRF identification approach for multi-degree-of-freedom (MDOF) structures under unknown input measurements using limited fused dynamic response measurements is proposed, where a double Chebyshev polynomial combined with an updated Extended Kalman filter (UEKF) approach is employed. The proposed approach is validated numerically with MDOF structures equipped with various nonlinear members, including MR damper (damping-dominant) and SMA damper (stiffness-dominant) at first. An experimental study on a four-story steel frame model structure equipped with a MR damper on its fourth story is carried out and the test measurement is employed to validate the proposed method by comparing the identified excitation, unknown acceleration response and MR damping force with test measurements. Results show that the proposed approach is capable of identifying the NRF provided by different dampers and unknown excitation nonparametrically, which is very helpful for post-event condition evaluation of engineering structures where structural nonlinearity should be considered but input is usually unknown.
Data-Driven Nonparametric Structural Nonlinearity Identification Under Unknown Excitation with Limited Data Fusion
In this paper, a nonparametric data-driven NRF identification approach for multi-degree-of-freedom (MDOF) structures under unknown input measurements using limited fused dynamic response measurements is proposed, where a double Chebyshev polynomial combined with an updated Extended Kalman filter (UEKF) approach is employed. The proposed approach is validated numerically with MDOF structures equipped with various nonlinear members, including MR damper (damping-dominant) and SMA damper (stiffness-dominant) at first. An experimental study on a four-story steel frame model structure equipped with a MR damper on its fourth story is carried out and the test measurement is employed to validate the proposed method by comparing the identified excitation, unknown acceleration response and MR damping force with test measurements. Results show that the proposed approach is capable of identifying the NRF provided by different dampers and unknown excitation nonparametrically, which is very helpful for post-event condition evaluation of engineering structures where structural nonlinearity should be considered but input is usually unknown.
Data-Driven Nonparametric Structural Nonlinearity Identification Under Unknown Excitation with Limited Data Fusion
Lecture Notes in Civil Engineering
Rainieri, Carlo (editor) / Fabbrocino, Giovanni (editor) / Caterino, Nicola (editor) / Ceroni, Francesca (editor) / Notarangelo, Matilde A. (editor) / Zhao, Ye (author) / Xu, Bin (author) / Deng, Baichuan (author) / He, Jia (author)
International Workshop on Civil Structural Health Monitoring ; 2021 ; Naples, Italy
2021-08-25
7 pages
Article/Chapter (Book)
Electronic Resource
English
Structural identification with unknown input excitation
| Springer Verlag | 2001
| SAGE Publications | 2013