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Recursive subspace identification for on-line tracking of structural modal parameter

机译:用于结构模态参数在线跟踪的递归子空间识别

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摘要

The objective of this paper is to develop an on-line tracking of system parameter estimation and damage detection techniques using response measurements. To avoid the singular-value-decomposition in data Hankel matrix, a new subspace identification algorithm was developed. Seismic response data of a 3-story steel frame with abrupt change of inter-story stiffness from the shaking table test was used to verify the proposed recursive subspace identification (RSI) method by using both input and output measurements. With the implementation of forgetting factor in RSI method the ability of on-line damage detection of the abrupt change of structural stiffness can be enhanced. Then, the recursive stochastic subspace identification (RSSI) algorithm is also developed for continuous structural health monitor of structure by using the output-only measurements. Verification of the proposed RSSI method by using the white noise response data of a 2-story reinforced concrete frame from its low level white noise excitation was used. Discussion of the subspace identification model parameters is also investigated.
机译:本文的目的是开发使用响应测量的系统参数估计和损坏检测技术的在线跟踪。为了避免数据汉克矩阵中的奇异值分解,提出了一种新的子空间识别算法。通过振动台试验,利用层间刚度突然变化的3层钢框架的地震响应数据,通过使用输入和输出测量值来验证所提出的递归子空间识别(RSI)方法。通过在RSI方法中实施遗忘因子,可以增强结构刚度突变的在线损伤检测能力。然后,还开发了递归随机子空间识别(RSSI)算法,通过使用仅输出的测量结果来进行结构的连续结构健康状况监视。通过使用两层钢筋混凝土框架的低白噪声激励对白噪声响应数据进行验证,对所提出的RSSI方法进行了验证。还讨论了子空间识别模型参数的讨论。

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