Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification
Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification
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DOI：
10.1002/stc.358
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发表时间：
2009-10
影响因子：
5.4
通讯作者：
E. P. Carden;A. Mita
中科院分区：
文献类型：
--
作者：
E. P. Carden;A. Mita
本文研究了目前的方法估计不确定性和置信区间的模态参数估计使用随机子空间识别。结果发现，大多数摄动方法已被限制到估计方差，而不是识别的模态参数的高阶矩。结果表明，模态参数可能呈现非正态分布，在这种情况下，方差不足以估计置信区间。目前的扰动方法扩展到估计更高的时刻，但它表明，估计的协方差函数的协方差的不准确性限制了这种方法的准确性。一个残留的自举方法，然后调查，然而，它被发现与数值和测量数据的残差并不都减少到白色系列。研究还发现，悬索桥的激励是非高斯分布的。这些是可靠地应用残余自举过程的严重困难。在此基础上，总结了目前大型民用基础设施模态参数估计的置信区间获取面临的挑战。版权所有© 2009约翰威利父子有限公司。
This paper examines current methods for estimating uncertainty and confidence intervals on modal parameters estimated using stochastic subspace identification. It is found that most perturbation methods have been limited to estimating variance and not higher moments of the identified modal parameters. It is shown that the modal parameters may exhibit non‐normal distributions and in such cases the variance is insufficient in estimating confidence intervals. A current perturbation method is extended to estimate higher moments but it is shown that the inaccuracy in estimating the covariance of the covariance function limits the accuracy of this approach. A residual bootstrapping approach is then investigated; however, it is found with both numerical and measured data that the residuals are not all reduced to white series. It is also found that the excitation of the suspension bridge investigated is non‐Gaussian distributed. These are serious difficulties in applying a residual bootstrapping procedure reliably. Based on these investigations, some current challenges in obtaining accurate confidence intervals on estimated modal parameters for large civil infrastructure are summarized. Copyright © 2009 John Wiley & Sons, Ltd.