A novel Bayesian approach for structural model updating utilizing statistical modal information from multiple setups
A novel Bayesian approach for structural model updating utilizing statistical modal information from multiple setups
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DOI：
10.1016/j.strusafe.2014.06.004
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发表时间：
2015
影响因子：
5.8
通讯作者：
W. Yan;L. Katafygiotis
中科院分区：
文献类型：
--
作者：
W. Yan;L. Katafygiotis
本文提出了一种利用多个装配的模态信息进行结构模型修正的快速贝叶斯方法。本文首次采用两阶段快速贝叶斯谱密度法来识别结构的最概然模态特性及其不确定性。模型更新问题，然后制定为一个最小化的目标函数，它可以包含统计信息的本地振型分量对应于不同的设置自动，没有事先组装或处理。提出了一种快速解析迭代格式来高效计算最优参数，以解决数值优化目标函数所需的计算负担。模型参数的后验不确定性也可以解析地导出，并且还适当地处理了估计指定协方差矩阵所需的高维Hessian矩阵的逆的计算困难。数值算例验证了这些方法的有效性和准确性。
In this paper, a fast Bayesian methodology is presented for structural model updating utilizing modal information from multiple setups. A two-stage fast Bayesian spectral density approach formulated recently is firstly employed to identify the most probable modal properties as well as their uncertainties. The model updating problem is then formulated as one minimizing an objective function, which can incorporate statistical information about local mode shape components corresponding to different setups automatically, without prior assembling or processing. A fast analytic-iterative scheme is proposed to efficiently compute the optimal parameters so as to resolve the computational burden required for optimizing the objective function numerically. The posterior uncertainty of the model parameters can also be derived analytically and the computational difficulty in estimating the inverse of the high dimensional Hessian matrix required for specifying the covariance matrix is also properly tackled. The efficiency and accuracy of all these methodologies are verified by numerical examples.