Hierarchical sparse Bayesian learning for structural damage detection: Theory, computation and application
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Huang et al. improved the SBL algorithm by eliminating two approximations in the theoretical formulation.
“Later, Huang et al. [212] improved the SBL algorithm by eliminating two approximations in the theoretical formulation.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [212]
The efficiency of the improved SBL algorithm was higher and its performance was better than that of the previous SBL method for a real structure with a significant modelling error.
“The efficiency of the improved algorithm was higher and its performance was better than that of the previous SBL method for a real structure with a significant modelling error.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [212]
Cites
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931 · see citing works
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Hou, R., & Xia, Y. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2020.115741 · Cited by 674 · see citing works
Review of finite element model updating methods for structural applications
Ereiz, S., Duvnjak, I., & Fernando Jiménez-Alonso, J. (2022) · Structures · doi:10.1016/j.istruc.2022.05.041 · Cited by 371 · see citing works
Ensemble classification method for structural damage assessment under varying temperature
Fallahian, M., Khoshnoudian, F., & Meruane, V. (2018) · Structural Health Monitoring · doi:10.1177/1475921717717311 · Cited by 60 · see citing works
Sparse Bayesian learning for structural damage detection using expectation–maximization technique
Hou, R., Xia, Y., Zhou, X., & Huang, Y. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2343 · Cited by 49 · see citing works
Self-citationLaplace approximation in sparse Bayesian learning for structural damage detection
Wang, X., Hou, R., Xia, Y., & Zhou, X. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106701 · Cited by 32 · see citing works
Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning
Huang, Y., Li, H., Wu, S., & Yang, Y. (2018) · Smart Materials and Structures · doi:10.1088/1361-665x/aac248 · Cited by 22 · see citing works
Self-citation
- Authors
- Huang, Y., Beck, J. L., & Li, H.
- Affiliations
- California Institute of Technology (US)
- Harbin Institute of Technology (CN)
- Corresponding author
- Beck, James L.
- Venue
- Structural Safety
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 64
- Pages
- 37-53
- ISSN
- 0167-4730 (ISSN-L)
- OpenAlex
- W2547960182
- Cited by
- 111
- References
- 1
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 15 · 2023: 9 · 2022: 9 · 2021: 10 · 2020: 18 · 2019: 8 · 2018: 11 · 2017: 4 · 2016: 1
- FWCI
- 10.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Safety (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (51308161)
- China Postdoctoral Council (20140018)
- 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 › Control Systems and Identification
- Keywords
- structural damage detection, sparse Bayesian learning, hierarchical Bayesian inference, ill-conditioning, modal data, steel frames, structural health monitoring, model error, damage localization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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