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Structural damage identification enabled by the non-parametric Bayesian method
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Qi’ang WANG1, Haobo WANG1, Mingli ZHOU2, Fayuan SUN2, Yiqing NI3, Ziyan WU4, Anchi DING1, Jianpeng LI1, Wenlei LI1
Journal of Vibration Engineering | 2025, 38(2) : 260 - 267
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Journal of Vibration Engineering | 2025, 38(2): 260-267
Structural damage identification enabled by the non-parametric Bayesian method
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Qi’ang WANG1, Haobo WANG1, Mingli ZHOU2, Fayuan SUN2, Yiqing NI3, Ziyan WU4, Anchi DING1, Jianpeng LI1, Wenlei LI1
Affiliations
  • 1.State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221008, China
  • 2.Xuzhou Traffic Engineering General Contracting Co., Ltd., Xuzhou 221003, China
  • 3.Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
  • 4.School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an 710072, China
Published: 2025-02-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.02.005
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Clustering analysis is a commonly used unsupervised method in data processing. However, the difficulty in accurately determining clustering parameters limits the application of this method in structural damage identification. To address this issue, a non-parametric Bayesian clustering method is proposed in this study, which combines structural modal parameters for structural damage identification and quantitative analysis, thereby expanding the application range of the non-parametric Bayesian model.First, the natural excitation method is used to extract the natural frequency from the measured vibration data of the structure.Then, the non-parametric Bayesian clustering method is employed to cluster the data. Finally, maximum likelihood heteroscedastic Gaussian process regression and Bayesian factors are combined to quantitatively analyze the clustering results for damage quantitation analysis. The results of the damage identification method are verified by the actual engineering case of Yonghe Bridge in Tianjin. The results show that this method can accurately cluster the natural frequency data and identify the different damage states of the structure without the need to pre-set clustering parameters.

structural health monitoring  /  damage identification  /  non-parametric Bayesian  /  Bayesian factor  /  modal parameter
Qi’ang WANG, Haobo WANG, Mingli ZHOU, Fayuan SUN, Yiqing NI, Ziyan WU, Anchi DING, Jianpeng LI, Wenlei LI. Structural damage identification enabled by the non-parametric Bayesian method[J]. Journal of Vibration Engineering, 2025 , 38 (2) : 260 -267 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.02.005
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Year 2025 volume 38 Issue 2
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.02.005
  • Receive Date：2024-03-02
  • Online Date：2026-02-11
  • Published：2025-02-10
Article Data
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History
  • Received：2024-03-02
  • Revised：2024-06-06
Funding
Affiliations
    1.State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221008, China
    2.Xuzhou Traffic Engineering General Contracting Co., Ltd., Xuzhou 221003, China
    3.Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
    4.School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an 710072, China
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