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Sensitive Damage Detection of Reinforced Concrete Bridge Slab by “Time-Variant Deconvolution” of SHF-Band Radar Signal.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Mar2019, Vol. 57 Issue 3, p1478-1488, 11p
- Publication Year :
- 2019
-
Abstract
- In this paper, we focus on ground-penetrating radar (GPR) for infrastructural health monitoring, especially for the monitoring of reinforced concrete (RC) bridge slab. Due to the demand of noncontact and high-speed monitoring technique which can handle vast amounts of aging infrastructures, GPR is a promising tool. However, because radar images consist of many reflected waves, they are usually difficult to interpret. Furthermore, the spatial resolution of system is not enough considering the thickness of target damages, cracks, and segregation are millimeter-to-centimeter order while the wavelength of ordinary GPR ultrahigh-frequency band is over 10 cm. To address these problems, for the purpose of sensitive damage detection, we propose a new algorithm based on deconvolution utilizing a super high-frequency (SHF) band system. First, a distribution of reflection coefficient is inversely estimated by 1-D bridge slab model. Because concrete is found to be a lossy medium at SHF band, we consider the attenuation of signal in deconvolution. The algorithm is called “time-variant deconvolution” in this paper. After the validation by simulation, the effects of the algorithm and frequency band on damage detection accuracy are evaluated by a field experiment. Though the results show a 1-mm horizontal crack is not detected by measured waves, when it is filled with water, it is detected by time-variant deconvolution. Moreover, the 1-mm dried crack is detected only by time-variant deconvolution at SHF band, which greatly emphasizes the peaks of the reflection coefficient of the crack. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 57
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 136509001
- Full Text :
- https://doi.org/10.1109/TGRS.2018.2866991