DBSCAN密度聚类算法支持的结构模态自动化识别
Automatic identification of structural modal supported by DBSCAN density clus-tering algorithm
常伟东 1黄金文2
作者信息
- 1. 北京城建华晟交通建设有限公司,北京 101300
- 2. 江西理工大学 土木与测绘工程学院,江西 赣州 341000
- 折叠
摘要
现有的模态识别方法在应用时往往需要基于人工从频谱中识别结构模态,然而人工识别模态费时费力,且存在主观性,其识别结果也并不一定准确.对此,提出了一种智能聚类算法支持的模态参数自动化识别方法.该方法首先构建了模态距离经验公式,然后对所有杂乱排布的稳定点进行DBSCAN密度聚类,最后引入三西格玛准则进一步提高结构模态自动化识别的鲁棒性.为了验证提出方法的有效性,对十层框架模型进行了模态参数自动化识别研究,结果表明,提出的方法能准确且自动地识别结构的模态参数,而无需人工介入.
Abstract
Existing modal identification methods often need manual identification of structural modal from the spectrum.However,manual identification is time-consuming,laborious,and subjective,and the identification results are not necessarily accurate.In this paper,an automatic identification method of modal parameters supported by in-telligent clustering algorithm is proposed.The method first constructs an empirical formula for modal distance,then performs DBSCAN density clustering on all the cluttered rows of stable points,and finally introduces the three-sig-ma criterion to further improve the robustness of the automatic identification of the structural modal.In order to vali-date the effectiveness of the proposed method,study on the automatic modal parameter identification is carried out on a ten-story frame model,and the results show that the proposed method can accurately and automatically identify the modal parameters of the structure without human intervention.
关键词
模态参数/密度聚类/自动识别/稳定图Key words
modal parameters/density clustering/automatic identification/stability map引用本文复制引用
出版年
2024
NETL
NSTL
万方数据