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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
pp. 4361-4377
S&M2415 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2989
Published: December 29, 2020

Bridge Damage Identification by Ground-based Synthetic Aperture Radar Using Blind Source Separation and Noise Reduction Technology [PDF]

Qian-Hao Cheng, Qiang Chen, Hui Wang, and Xiang-Lei Liu

(Received July 22, 2020; Accepted November 27, 2020)

Keywords: damage identification of bridges, blind source separation, fast Fourier transform, ground-based synthetic aperture radar, second-order blind identification

Ground-based synthetic aperture radar (GBSAR) is regarded as an important monitoring technique for bridge damage identification. However, the interference effect of noise signals on bridge damage identification reduces its effectiveness. In this study, we proposed a blind source separation (BSS) technology based on a second-order blind identification (SOBI) algorithm, which was applied to bridge damage identification with GBSAR. We used two groups of simulated experiments with different frequencies to verify the feasibility of this method. Then, we conducted an experiment using actual GBSAR data for the bridge. To verify the effectiveness of the algorithm, we compared the frequencies of the bridge signals that were identified by BSS and fast Fourier transform (FFT). The results showed that the frequency of the damaged bridge was 7.324% higher than that of the healthy bridge, and the frequency increase of the monitoring signal processed by BSS was clearly accurate. The frequency of the damaged bridge was 59.819% higher than that of the healthy bridge. These findings showed that the SOBI method can be used to separate signals in order to obtain the signal source of the damage more efficiently, reduce the interference of other signal sources, and significantly improve the effectiveness of bridge damage identification.

Corresponding author: Xiang-Lei Liu


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Cite this article
Qian-Hao Cheng, Qiang Chen, Hui Wang, and Xiang-Lei Liu, Bridge Damage Identification by Ground-based Synthetic Aperture Radar Using Blind Source Separation and Noise Reduction Technology, Sens. Mater., Vol. 32, No. 12, 2020, p. 4361-4377.



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