Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning
Abstract
Abstract Sensitivity to damage and robustness to noise are critical requirements for the effectiveness of structural damage detection. In this study, a two-stage damage identification method based on the fractal dimension analysis and multi-task Bayesian learning is presented. The Higuchi’s fractal dimension (HFD) based damage index is first proposed, directly examining the time-frequency characteristic of local free vibration data of structures based on the irregularity sensitivity and noise robustness analysis of HFD. Katz’s fractal dimension is then presented to analyze the abrupt irregularity change of the spatial curve of the displacement mode shape along the structure. At the second stage, the multi-task sparse Bayesian learning technique is employed to infer the final damage localization vector, which borrow the dependent strength of the two fractal dimension based damage indication information and also incorporate the prior knowledge that structural damage occurs at a limited number of locations in a structure in the absence of its collapse. To validate the capability of the proposed method, a steel beam and a bridge, named Yonghe Bridge, are analyzed as illustrative examples. The damage identification results demonstrate that the proposed method is capable of localizing single and multiple damages regardless of its severity, and show superior robustness under heavy noise as well.
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Huang et al. used a multi-task SBL to fuse the respective strengths of two FD-based damage indices to improve the reliability of damage localisation.
“To improve the reliability of damage localisation, Huang et al. [213] used a multi-task SBL to fuse the respective strengths of two FD-based damage indices.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [213]
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- Authors
- Huang, Y., Li, H., Wu, S., & Yang, Y.
- Affiliations
- Harbin Institute of Technology (CN)
- The Institute of Statistical Mathematics (JP)
- Los Alamos National Laboratory (US)
- Corresponding author
- Huang, Yong
- Venue
- Smart Materials and Structures
- Publisher
- IOP Publishing
- Published
- 2018-06-01
- Volume
- 27
- Issue
- 7
- Pages
- 075020
- ISSN
- 0964-1726 (ISSN-L)
- 1361-665X
- OpenAlex
- W2802438791
- Cited by
- 22
- References
- 35
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 1 · 2020: 8 · 2019: 1 · 2018: 2
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Smart Materials and Structures (publishedVersion)
- Funders
- National Natural Science Foundation of China (51308161)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Bladed Disk Vibration Dynamics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- structural damage detection, damage localization, fractal dimension, Higuchi's fractal dimension, sparse Bayesian learning, multi-task learning, free vibration, noise robustness, steel beams, bridges
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