Abstract:Within the framework of probability and information theory, this paper presents a methodology for finiteelement (FE) modelclass selection for selecting suitable modeling parameters to update FE models based on Bayesian evidence inference and Markov chain Monte Carlo (MCMC) method. The amount of information needed to be extracted from the measurement data is explicitly quantified during the procedure of FE model updating by introducing the concept of information divergence. This is then employed for penalizing the complexity of FE parameterization with a tradeoff between the complexity of a given FE model class and that of its corresponding informationtheoretic interpretation, in order to obtain a relatively simple FE parameterization scheme for keeping similar modeldata matching and avoid the overfitting problem arisen from excessive modeling parameters efficiently. The validity of the presented methodology was verified through both numerical simulation and experimental verification carried out for a twostory boltconnected steel frame.
尹涛 王祥宇 周越. 基于Bayesian证据推断与信息增益的参数化有限元修正模型选择[J]. 振动与冲击, 2018, 37(12): 159-166.
YIN Tao ZHOU Yue WANG Xiang-yu. Model selection in updating of parametric FE model based on Bayesian evidence inference and information divergence#br#. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(12): 159-166.
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