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Automation in Bayesian operational modal analysis using clustering-based interpretation of stabilization diagram

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A Correction to this article was published on 07 January 2023

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Abstract

Long-term structural health monitoring (SHM) requires processing a large amount of data with human intervention often involved. Operational modal analysis (OMA) is generally treated as prerequisite for vibration-based SHM to obtain modal parameters, including natural frequency, damping ratio and mode shape. Bayesian modal analysis has received considerable attention in recent years, as it identifies modal parameters but also provides their uncertainties. In conventional Bayesian OMA, manual operation includes initial frequency and bandwidth selection. The former is often visually picked from a singular value spectrum, while the latter is chosen with the consideration of a trade-off between the data used for making inference and modeling error involved. The above procedures have limited the application of Bayesian approach in processing long-term data. This work aims at developing an automation technique for Bayesian fast Fourier transform modal identification. A stabilization diagram is first built and automatically interpreted by modal validation criteria and clustering strategy to obtain the initial frequency. Spurious modes are also cleared in this step. A series of effective bandwidth factors within a predefined factor range are then determined for the selection of frequency band. The proposed automation method is verified by a numerical example and then applied on the Z24 benchmark bridge for long-term data analysis. Results show that the automation method is able to accurately identify modal parameters with minimum human intervention, even for closely spaced and weakly excited modes. It is also shown that the method is adequate to automatically analyze long-term monitoring data.

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Data available statement

Data, models, and codes generated and used during the study are available from the corresponding author upon reasonable request.

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Acknowledgements

The first author would like to express gratitude and sincere appreciation for the partial financial support by the University of Louisville. The second author is funded by the National Key R&D Program of China (2019YFB2102702) and Shenzhen Science and Technology Program (JSGG20210802093207022). Authors would also like to acknowledge KU. Leuven for the open access data used in this paper.

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Authors and Affiliations

  1. Department of Civil and Environment Engineering, University of Louisville, Louisville, 40292, KY, USA

    Jice Zeng & Young Hoon Kim

  2. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China

    Yan-Long Xie

  3. Design and Information Department, China Railway South Investment Group Co.Ltd, Shenzhen, 518000, China

    Junfang Wang

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  1. Jice Zeng
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Correspondence to Yan-Long Xie.

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Zeng, J., Xie, YL., Kim, Y.H. et al. Automation in Bayesian operational modal analysis using clustering-based interpretation of stabilization diagram. J Civil Struct Health Monit 13, 443–467 (2023). https://doi.org/10.1007/s13349-022-00644-7

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  • DOI: https://doi.org/10.1007/s13349-022-00644-7

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