...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Earthquake Engineering & Structural Dynamics >Stochastic subspace identification for output-only modal analysis: application to super high-rise tower under abnormal loading condition
【24h】

Stochastic subspace identification for output-only modal analysis: application to super high-rise tower under abnormal loading condition

机译：仅用于输出模态分析的随机子空间识别：异常载荷条件下在超高层塔中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The objective of this paper is to develop an online system parameter estimation technique from the response measurements through using the recursive covariance-driven stochastic subspace identification (SSI-COV) approach. In developing the recursive SSI-COV, to avoid time-consumption of singular value decomposition in recursive SSI, the extended instrumental variable version of the projection approximation subspace tracking method is used in SSI-COV. Besides, to reduce the effect of noise on the results of identification, the preprocessing of data using recursive singular spectrum analysis technique is also presented to remove the noise contaminant measurements to enhance the stability of data analysis. On the basis of the proposed method, both the ambient vibration and seismic response data of a tower (Canton Tower) are used to observe the time-varying system natural frequencies of a tower from its operating condition. Results from using off-line SSI-COV method under normal operating condition are also presented. Comparison on the identified time-varying dynamic characteristics of the tower under normal operating condition and earthquake response of distanced earthquake event is discussed.
机译：本文的目的是通过使用递归协方差驱动的随机子空间识别（SSI-COV）方法从响应测量值开发一种在线系统参数估计技术。在开发递归SSI-COV时，为了避免递归SSI中奇异值分解的时间消耗，在SSI-COV中使用了投影近似子空间跟踪方法的扩展工具变量版本。此外，为了减少噪声对识别结果的影响，还提出了使用递归奇异频谱分析技术对数据进行预处理，以消除噪声污染物的测量结果，以提高数据分析的稳定性。在此方法的基础上，使用塔（广州塔）的环境振动和地震响应数据，从塔的运行状态观察塔的时变系统固有频率。还介绍了在正常工作条件下使用离线SSI-COV方法的结果。讨论了在正常工作条件下确定的塔的时变动力特性与远距离地震事件的地震响应的比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号