[1]祝青鑫,王浩,茅建校,等.基于聚类分析的桥梁结构模态参数自动识别方法[J].东南大学学报(自然科学版),2020,50(5):837-843.[doi:10.3969/j.issn.1001-0505.2020.05.007]
　Zhu Qingxin,Wang Hao,Mao Jianxiao,et al.Automated modal parameter identification method for bridges based on cluster analysis[J].Journal of Southeast University (Natural Science Edition),2020,50(5):837-843.[doi:10.3969/j.issn.1001-0505.2020.05.007]
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基于聚类分析的桥梁结构模态参数自动识别方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第5期
页码:
837-843
栏目:
土木工程
出版日期:
2020-09-20

文章信息/Info

Title:
Automated modal parameter identification method for bridges based on cluster analysis
作者:
祝青鑫1; 王浩1; 茅建校1; 胡所亭2; 赵欣欣2; 潘永杰2
1东南大学混凝土与预应力混凝土结构教育部重点实验室, 南京 210096; 2中国铁道科学研究院集团有限公司, 北京 730050
Author(s):
Zhu Qingxin1;  Wang Hao1;  Mao Jianxiao1;  Hu Suoting2;  Zhao Xinxin2;  Pan Yongjie2
1Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
2China Academy of Railway Sciences Corporation Limited, Beijing 730050, China
关键词:
模态参数;  稳定图;  随机子空间(SSI);  聚类分析;  自动识别
Keywords:
modal parameters;  stabilization diagram;  stochastic subspace identification(SSI);  cluster analysis;  automated identification
分类号:
TU317.2
DOI:
10.3969/j.issn.1001-0505.2020.05.007
摘要:
为实现桥梁结构模态参数的自动识别,基于随机子空间法(SSI)生成的稳定图,综合采用主成分分析(PCA)、k均值聚类法和层次聚类法,提出了一种桥梁结构模态参数自动识别方法.首先,基于模态验证准则(MVC)向量的PCA分析结果,借助k均值聚类法初步剔除稳定图中的虚假模态.然后,分析层次树截断簇数和有效模态数量的关系,得到最优截断簇数的确定准则.最后,建立了桥梁结构模态参数自动识别方法,并采用缩尺模型试验和实测桥梁响应数据进行验证.结果表明,所提方法能够有效剔除稳定图中的虚假模态,自动确定有效模态数量,提升了SSI生成的稳定图处理过程中的自动化程度,实现了基于环境激励下结构响应数据的桥梁结构模态参数自动识别.
Abstract:
To realize the automated identification of modal parameters for bridges, according to the stabilization diagram produced by stochastic subspace identification(SSI), an automated modal parameter identification method for bridges was proposed based on principal component analysis(PCA), k-means clustering method and hierarchical clustering method. First, according to the principal components of the modal validation criteria(MVC)produced by PCA, the false modes in the stabilization diagram were pre-eliminated by using the k-means clustering method. Then, the relationship between the number of the truncated clusters and the number of the effective modes was studied to determine the optimal number of clusters for hierarchical clustering. Finally, an automated modal parameter identification method for bridges was established. The scaled-model tests and field measurements of a railway bridge were carried to verify the proposed method. The results indicate that the false modes in the stabilization diagram can be effectively removed by using the proposed method. The number of the effective modes can be determined. The automation in the process of stabilization diagram produced by SSI can be improved. The automated modal identification of structural modal parameters of bridges based on field measurements is realized.

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相似文献/References:

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　Zheng Peijuan,Lin Dinan,Zong Zhouhong,et al.Automatic stochastic subspace identification of modal parameters based on graph clustering[J].Journal of Southeast University (Natural Science Edition),2017,47(5):710.[doi:10.3969/j.issn.1001-0505.2017.04.014]
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备注/Memo

备注/Memo:
收稿日期: 2020-04-07.
作者简介: 祝青鑫(1992—), 男, 博士生;王浩(联系人), 男, 博士, 研究员, 博士生导师, wanghao1980@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51722804, 51978155)、江苏省重点研发计划资助项目(BE2018120)、江苏省研究生科研创新计划资助项目(KYCX19_0095)、连镇铁路五峰山长江特大桥科技研究开发计划资助项目(2019123).
引用本文: 祝青鑫,王浩,茅建校,等.基于聚类分析的桥梁结构模态参数自动识别方法[J].东南大学学报(自然科学版),2020,50(5):837-843. DOI:10.3969/j.issn.1001-0505.2020.05.007.
更新日期/Last Update: 2020-09-20