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10 April 2014 Efficient model updating using Bayesian probabilistic framework based on measured vibratory response
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Abstract
Currently, the deviation between the model and an actual structure is generally identified through a so-called model updating process, in which a set of experimental measurement of structural dynamic response is used in combination with the model prediction to facilitate an inverse analysis that is usually deterministic. In reality, however, structural properties, such as mass and stiffness, are inevitably subject to variation/uncertainties. As such, the identification of property variations in a probabilistic manner can truly reveal the underlying physical characteristics of the structure involved. In this research, we adopt the Bayesian probabilistic framework to conduct stochastic model updating using measured vibratory response. Furthermore, this paper proposes an efficient scheme to facilitate such procedures by incorporating the Gaussian process and Markov Chain Monte Carlo (MCMC) into the Bayesian framework. The feasibility of this presented methodology is validated by case studies.
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K. Zhou, G. Liang, and J. Tang "Efficient model updating using Bayesian probabilistic framework based on measured vibratory response", Proc. SPIE 9063, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014, 90631T (10 April 2014); https://doi.org/10.1117/12.2045134
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