期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 75, 期 -, 页码 27-40出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2015.12.024
关键词
Probabilistic model updating; Bayesian inference; Markov chain Monte Carlo; Modal identification; Iterated improved reduced system; Structural health monitoring
资金
- Royal Dutch Shell through the MIT Energy Initiative
This paper investigates a new probabilistic strategy for Bayesian model updating using incomplete modal data. Direct mode matching between the measured and the predicted modal quantities is not required in the updating process, which is realized through model reduction. A Markov chain Monte Carlo technique with adaptive random-walk steps is proposed to draw the samples for model parameter uncertainty quantification. The iterated improved reduced system technique is employed to update the prediction error as well as to calculate the likelihood function in the sampling process. Since modal quantities are used in the model updating, modal identification is first carried out to extract the natural frequencies and mode shapes through the acceleration measurements of the structural system. The proposed algorithm is finally validated by both numerical and experimental examples: a 10-storey building with synthetic data and a 8-storey building with shaking table test data. Results illustrate that the proposed algorithm is effective and robust for parameter uncertainty quantification in probabilistic model updating of buildings. (C) 2016 Elsevier Ltd. All rights reserved.
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