@inproceedings{4f7cf8b0981742309818c6b3e82b148a,
title = "Efficient model updating using Bayesian probabilistic framework based on measured vibratory response",
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.",
keywords = "Bayesian probabilistic framework, Gaussian process, Markov Chain Monte Carlo, Measured vibratory response, Model updating, Uncertainties",
author = "K. Zhou and G. Liang and J. Tang",
year = "2014",
doi = "10.1117/12.2045134",
language = "English",
isbn = "9780819499899",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
booktitle = "Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014",
address = "United States",
note = "Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014 ; Conference date: 10-03-2014 Through 13-03-2014",
}