Cited by 24Structural Health Monitoring TechniquesMachine Fault Diagnosis TechniquesSeismic Performance and Analysis

Structural damage identification method based on vibration statistical indicators and support vector machine

Lei, Jiayan, Cui, Yiguan, Shi, Wei·2022·Advances in Structural Engineering

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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Crossref · 10.1177/13694332221073983

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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
Venue
Advances in Structural Engineering
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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