Modal identification of non‐classically damped structures using generalized sparse component analysis

Author:

Yao Xiao‐Jun12,Yi Ting‐Hua2,Qu Chun‐Xu2,Li Hong‐Nan2

Affiliation:

1. School of Civil and Transportation Engineering Hebei University of Technology Tianjin China

2. School of Civil Engineering Dalian University of Technology Dalian China

Abstract

SummaryModal identification method based on blind source separation (BSS) technique has gained extensive attentions for civil structures. Developing the complex modes estimation method is important in practical applications because the assumption of proportional damping is not always satisfied. Sparse component analysis (SCA) performs well in underdetermined BSS problems. However, SCA is confined to the situation of proportional damping. In this study, a generalized SCA method is proposed to extend the original SCA method to both real and complex modes identification. First, the general formulation of complex modes is extended by the analytic form to eliminate the complex conjugate part in the BSS model. A new single‐source‐point detection method that is available to handle real and complex modes is proposed. Local outlier factor method is adopted to remove the outliers in single source points. Subsequently, complex‐valued modal matrix is calculated by the clustering technique. Then, modal responses are recovered using the complex version of smoothed zero norm method, where modal frequencies and damping ratios can be extracted. Finally, the effectiveness of the proposed method is demonstrated for identification of real and complex modes, close modes, and underdetermined problem. The application to a benchmark structure demonstrates the effectiveness for practical applications.

Funder

National Natural Science Foundation of China

Liaoning Revitalization Talents Program

Natural Science Foundation of Hebei Province

Publisher

Wiley

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unsupervised structural damage identification based on covariance matrix and deep clustering;The Structural Design of Tall and Special Buildings;2024-04-23

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