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Smart Structures and Systems Volume 24, Number 2, August 2019 , pages 243-256 DOI: https://doi.org/10.12989/sss.2019.24.2.243 |
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Gaussian mixture model for automated tracking of modal parameters of long-span bridge |
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Jian-Xiao Mao, Hao Wang and Billie F. Spencer Jr.
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| Abstract | ||
| Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure. | ||
| Key Words | ||
| Gaussian Mixture Model (GMM); baseline modal properties; automated mode tracking; long-span bridge; structural health monitoring (SHM) | ||
| Address | ||
| Jian-Xiao Mao: Key Laboratory of C&PC Structures of Ministry of Education, Southeast University, Nanjing 211189, China; Nathan M. and Anne M. Newmark Endowed Chair of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A. Hao Wang: Key Laboratory of C&PC Structures of Ministry of Education, Southeast University, Nanjing 211189, China Billie F. Spencer Jr.: Nathan M. and Anne M. Newmark Endowed Chair of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A. | ||