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Smart Structures and Systems Volume 17, Number 3, March 2016 , pages 445-470 DOI: https://doi.org/10.12989/sss.2016.17.3.445 |
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Investigation of modal identification and modal identifiability of a cable-stayed bridge with Bayesian framework |
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Sin-Chi Kuok and Ka-Veng Yuen
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| Abstract | ||
| In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed. | ||
| Key Words | ||
| Bayesian inference; structural health monitoring; cable-stayed bridge; modal identification; modal identifiability | ||
| Address | ||
| Sin-Chi Kuok:Department of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA 14853 Ka-Veng Yuen: Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Macao, China | ||