桥梁结构模态参数识别作为桥梁健康监测系统的重要组成部分之一，对桥梁的长期健康监测具有重要意义。而现有的模态参数识别算法还不能实现结构系统阶次和模态参数的自动化识别，基于此，本文先简单介绍了随机子空间算法的基本原理；其次通过引入“滑窗技术”以实现对时变结构模态参数的在线识别；接着针对稳定图定阶难这一问题，提出了基于奇异熵理论的系统定阶算法，以实现系统阶次的自动化确定；接着又将统计学中的“谱系聚类算法”与随机子空间算法进行结合，实现桥梁结构模态参数的自动化识别。最后利用本文算法识别苏通大桥在竖桥向和横桥向的模态参数结果，并将其与理论计算值进行对比分析，结果表明：本文算法不仅能实现桥梁结构系统阶次的自动化确定，还能实现模态参数的自动化识别，方便对桥梁结构参数的实时监控，了解桥梁结构的健康运营状态。
Abstract
As one of the important parts of bridge health monitoring system, the bridge structure modal parameters identification has great signification for the long-term health monitoring of bridge. However, the existing modal parameter identification algorithms cannot realize the adaptive decomposition of structural response signal and automatic identification of modal parameters. In that case, this article provides a brief introduction to the basic principle of stochastic subspace algorithm firstly; And then the sliding window was introduced in order to realize the on-line identification of time-varying structure modal parameters; And the system order algorithm was put forward based on the singular entropy theory that is aimed at the problem of ordering difficulty, so as to realize the automation system order determine; Finally, it was the hierarchical clustering algorithm in statistics combined with random subspace algorithm that to realize the automation identification of bridge structure modal parameters. At last, the proposed algorithm in this article was used to identify the modal parameters of the Sutong Bridge in vertical and transverse direction, and the identify results were analyzed comparing with the real value, the results show that this proposed algorithm can not only realize the automatically determine of system order, also can realize the automatic identifying of modal parameters as well as real-time monitoring of the bridge structure parameters, so that to real-time monitoring the running status of the bridge structure.
关键词
桥梁结构 /
滑窗技术 /
随机子空间算法 /
模态参数 /
谱系聚类算法
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Key words
Bridge structure
/
Sliding window technique /
Stochastic subspace identification /
Modal parameter /
Hierarchical clustering algorithm
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