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Enhancing the performance of stochastic subspace identification method via energy-oriented categorization of modal components

机译:通过模拟分组提高随机子空间识别方法的性能分类

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摘要

As an advanced modal identification method, Stochastic Subspace Identification (SSI) technique has been widely used in the engineering field in recent years. However, to apply this technique properly, the characteristics of external force acting on the structural system and the data quality of response signals should meet certain requirements, which sometimes cannot be well satisfied. This may result in some problematic issues, such as mode absences. To improve the working performance of standard SSI method, this study proposes an implementation strategy which is characterized by categorization of modal components in the response into different energy groups for data preprocessing. A numerical case study is presented first, to demonstrate that a key reason for the problematic issues encountered by the standard SSI technique is the imbalanced distribution of energies associated with different modal components of the system response. The proposed method is then detailed. To implement this method, the system response should be first divided into a number of component groups with each group consisting of certain modal components, whose spectral energies are in similar order of magnitude. Then, the standard SSI technique is utilized to identify the modal parameters for each of the above component groups. Measured responses from a real super-tall building are used in the proposed method and other modal identification techniques. The results are compared to validate and evaluate the performance of the proposed method.
机译:作为一种先进的模态识别方法,近年来,随机子空间识别(SSI)技术已广泛应用于工程领域。然而,为了适当地应用该技术,作用在结构系统上的外力特性和响应信号的数据质量应满足某些要求,这有时不能充分满足。这可能导致一些有问题的问题,例如模式缺席。为了提高标准SSI方法的工作性能,本研究提出了一种实施策略,其特征在于,以数据预处理的不同能量组的响应分类为特征。首先提出了一个数字案例研究,以证明标准SSI技术遇到的有问题问题的关键原因是与系统响应的不同模态分量相关的能量的不平衡分布。然后详细说明该方法。为了实现该方法,应该首先将系统响应分成多个组件组,其中每个组由某些模态分量组成,其频谱能量是相似的数量级。然后,使用标准SSI技术来识别上述每个组件组的模态参数。从真实超高建筑物的测量响应用于所提出的方法和其他模态识别技术。比较结果以验证和评估所提出的方法的性能。

著录项

  • 来源
    《Engineering Structures》 |2021年第15期|111917.1-111917.9|共9页
  • 作者单位

    Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou 510006 Guangdong Peoples R China;

    Chongqing Univ MOE Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China|Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China;

    Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou 510006 Guangdong Peoples R China;

    Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou 510006 Guangdong Peoples R China;

    Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Stochastic subspace identification; Mode absence; Modal identification; Zero-phase filtering; High-rise building;

    机译:随机子空间识别;模式缺席;模态识别;零相滤波;高层建筑;

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