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Structural damage identification method based on vibration statistical indicators and support vector machine
https://orcid.org/0000-0003-3738-926X
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.
Structural damage identification method based on vibration statistical indicators and support vector machine
https://orcid.org/0000-0003-3738-926X
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.
Structural damage identification method based on vibration statistical indicators and support vector machine
https://orcid.org/0000-0003-3738-926X
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.
Structural damage identification method based on vibration statistical indicators and support vector machine
Lei, Jiayan (author) / Cui, Yiguan (author) / Shi, Wei (author)
Advances in Structural Engineering ; 25 ; 1310-1322
2022-04-01
13 pages
Article (Journal)
Electronic Resource
English
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