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Modal identification of civil structures via covariance-driven stochastic subspace method

Guangzhou University-Tamkang University Joint Research Center for Engineering Structure Disaster Prevention and Control, Guangzhou University, Guangzhou, Guangdong 510006, China

Special Issues: Mathematical Methods in Civil Engineering

It is usually of great importance to identify modal parameters for dynamic analysis and vibration control of civil structures. Unlike the cases in many other fields such as mechanical engineering where the input excitation of a dynamic system may be well quantified, those in civil engineering are commonly characterized by unknown external forces. During the last two decades, stochastic subspace identification (SSI) method has been developed as an advanced modal identification technique which is driven by output-only records. This method combines the theory of system identification, linear algebra (e.g., singular value decomposition) and statistics. Through matrix calculation, the so-called system matrix can be identified, from which the modal parameters can be determined. The SSI method can identify not only the natural frequencies but also the modal shapes and damping ratios associated with multiple modes of the system simultaneously, making it of particular efficiency. In this study, main steps involved in the modal identification process via the covariance-driven SSI method are introduced first. A case study is then presented to demonstrate the accuracy and efficiency of this method, through comparing the corresponding results with those via an alternative method. The effects of noise contaminated in output signals on identification results are stressed. Special attention is also paid to how to determine the mode order accurately.
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Keywords modal identification; stochastic subspace identification; civil structure; covariance driven; output only; operational modal analysis

Citation: Zhi Li, Jiyang Fu, Qisheng Liang, Huajian Mao, Yuncheng He. Modal identification of civil structures via covariance-driven stochastic subspace method. Mathematical Biosciences and Engineering, 2019, 16(5): 5709-5728. doi: 10.3934/mbe.2019285


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