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Innovative stabilization diagram for automated structural modal identification based on ERA and hierarchical cluster analysis

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Abstract

As modal parameters are the essential features of structural operational condition, the automated structural modal identification technique is currently driving a strong interest in the field of vibration-based structural health monitoring (SHM). This task is highly judgmental, with user expertise playing an important role as to which estimated mode is being selected. To simplify the identification procedure and improve the identification accuracy, this paper presents an innovative stabilization diagram for operational modal analysis (OMA) based on Natural excitation technique (NExT)/eigensytem realization algorithm (ERA) and hierarchical cluster analysis. It consists of three key steps: (1) Physical modes are filtered preliminarily by a novel distance index based on output matrix from Consistent Mode Indicator (CMI_O); (2) Hierarchical clustering analysis is developed for automatic interpretation of the traditional stabilization diagram; (3) Thompson-Tau technique and a regrouping procedure are introduced for further screening outliers to improving the identification accuracy. Numerically generated data, experimental data obtained from a four-layer structure in the lab and field monitored data from a super high-rise structure are used to verify the applicability and reliability of the proposed method. The results show that the proposed method is able to identify physical modes in high accuracy even with noise interference, enabling the detection of good flexibility and practicability. Furthermore, none of user-defined parameter and less expert experience is demanded for the improved method.

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Acknowledgements

The work described in this paper was financially supported by the National Natural Science Foundation of China (Grant No. 51608136 and 51908149), Shenzhen Science Technology and Innovation Commission (SZSTI) Basic Research General Program (Grant No. JCYJ20190808154411663), and Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering (SZU) (Grant No. 2020B1212060074), for which, the writers are grateful. The authors thank all the reviewers and editors for their significant help and useful suggestions.

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Authors and Affiliations

  1. School of Civil Engineering, Guangzhou University, Guangzhou, People’s Republic of China

    Xijun Ye, Peili Huang & Chudong Pan

  2. Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen University, Shenzhen, People’s Republic of China

    Liu Mei

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  1. Xijun Ye
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Correspondence to Xijun Ye.

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Ye, X., Huang, P., Pan, C. et al. Innovative stabilization diagram for automated structural modal identification based on ERA and hierarchical cluster analysis. J Civil Struct Health Monit 11, 1355–1373 (2021). https://doi.org/10.1007/s13349-021-00514-8

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