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Proceedings Paper

Detecting subsurface features and distresses of roadways and bridge decks with ground penetrating radar at traffic speed
Author(s): Hao Liu; Ralf Birken; Ming L. Wang
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Paper Abstract

This paper presents the detections of the subsurface features and distresses in roadways and bridge decks from ground penetrating radar (GPR) data collected at traffic speed. This GPR system is operated at 2 GHz with a penetration depth of 60 cm in common road materials. The system can collect 1000 traces a second, has a large dynamic range and compact packaging. Using a four channel GPR array, dense spatial coverage can be achieved in both longitudinal and transversal directions. The GPR data contains significant information about subsurface features and distresses resulting from dielectric difference, such as distinguishing new and old asphalt, identification of the asphalt-reinforced concrete (RC) interface, and detection of rebar in bridge decks. For roadways, the new and old asphalt layers are distinguished from the dielectric and thickness discontinuities. The results are complemented by surface images of the roads taken by a video camera. For bridge decks, the asphalt-RC interface is automatically detected by a cross correlation and Hilbert transform algorithms, and the layer properties (e.g., dielectric constant and thickness) can be identified. Moreover, the rebar hyperbolas can be visualized from the GPR B-scan images. In addition, the reflection amplitude from steel rebar can be extracted. It is possible to estimate the rebar corrosion level in concrete from the distribution of the rebar reflection amplitudes.

Paper Details

Date Published: 12 April 2017
PDF: 12 pages
Proc. SPIE 10168, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 1016812 (12 April 2017); doi: 10.1117/12.2272735
Show Author Affiliations
Hao Liu, China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd. (China)
Northeastern Univ. (United States)
Ralf Birken, StreetScan, Inc. (United States)
Ming L. Wang, Northeastern Univ. (United States)


Published in SPIE Proceedings Vol. 10168:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017
Jerome P. Lynch, Editor(s)

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