A new gibbs-sampling based algorithm for Bayesian Model updating of linear dynamic systems with incomplete complex modal data
Model updating using measured system dynamic response has a wide range of applications in structural health monitoring, control and response prediction. In this paper, we are interested in model updating of a linear structural dynamic system with non-classical damping based on incomplete modal...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/105851 http://hdl.handle.net/10220/17969 http://www.iaeng.org/publication/IMECS2013/ |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Model updating using measured system dynamic
response has a wide range of applications in structural health
monitoring, control and response prediction. In this paper, we
are interested in model updating of a linear structural dynamic
system with non-classical damping based on incomplete modal
data including modal frequencies, damping ratios, and partial
complex mode shapes of some of the dominant modes. To
quantify the uncertainties and plausibility of the model
parameters, a Bayesian approach is adopted. A new Gibbssampling
based algorithm is proposed that allows for an
efficient update of the probability distribution of the model
parameters. The effectiveness and efficiency of the proposed
method are illustrated by a numerical example involving a
linear structural dynamic system with complex modes. |
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