Structural damage identification method based on vibration statistical indicators and support vector machine
Abstract
This study presents a support vector machine (SVM) based structural damage detection approach for a steel frame model. Totally 10 structural scenarios including one undamaged and nine damaged cases (D1–D9) are defined by replacing different column elements to reduce the lateral stiffness between layers. Sensors are laid out at each layer of the frame structure to pick up acceleration response stirred up by the simulated white noise excitations. Statistical analysis is carried out and three damage indictor features are extracted from 800 samples of record duration. Subsequently, the SVM classifiers are developed to conduct a series of indictor fusion schemes to identify multiple damage states. The results indicate that the approach has good identification performance with damage features from three indicators, especially for a high dimensional input eigenvector. Meanwhile, acceptable detection accuracy can be obtained in situations with incomplete measurement; the robustness in terms of the resistance against noise is also discussed.
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SVM is beneficial for pattern recognition and can be used for regression analysis.
“The technique is beneficial for pattern recognition and can be used for regression analysis [242-244].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [243]
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- Authors
- Lei, J., Cui, Y., & Shi, W.
- Affiliations
- Xiamen University (CN)
- Hezhong Architecture Design Institute, Fuzhou, China
- Corresponding author
- Lei, Jiayan
- ORCID
- Venue
- Advances in Structural Engineering
- Publisher
- SAGE Publications
- Published
- 2022-03-06
- Volume
- 25
- Issue
- 6
- Pages
- 1310-1322
- ISSN
- 1369-4332 (ISSN-L)
- 2048-4011
- OpenAlex
- W4221112900
- Cited by
- 24
- References
- 25
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 4 · 2023: 6 · 2022: 3
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advances in Structural Engineering (publishedVersion)
- Funders
- Xiamen Engineering Technology Center for Intelligent Maintenance of Infrastructures (TCIMI201806)
- National Natural Science Foundation of China (U2005216)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Seismic Performance and Analysis
- Keywords
- structural damage detection, support vector machine, steel frames, damage index, incomplete measurements, noise robustness, column damage
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