Gaussian mixture model for automated tracking of modal parameters of long-span bridge

Jian Xiao Mao, Hao Wang, Billie F. Spencer

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)243-256
Number of pages14
JournalSmart Structures and Systems
Volume24
Issue number2
DOIs
StatePublished - Jan 1 2019

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Learning systems
Monitoring
Structural health monitoring
Temperature

Keywords

  • Automated mode tracking
  • Baseline modal properties
  • Gaussian Mixture Model (GMM)
  • Long-span bridge
  • Structural health monitoring (SHM)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Gaussian mixture model for automated tracking of modal parameters of long-span bridge. / Mao, Jian Xiao; Wang, Hao; Spencer, Billie F.

In: Smart Structures and Systems, Vol. 24, No. 2, 01.01.2019, p. 243-256.

Research output: Contribution to journalArticle

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