DocumentCode :
1408723
Title :
Automatic Detection of Bridge Deck Condition From Ground Penetrating Radar Images
Author :
Wang, Zhe Wendy ; Zhou, MengChu ; Slabaugh, Greg ; Zhai, Jiefu ; Fang, Tong
Author_Institution :
ID Syst., Inc., Cranbury, NJ, USA
Volume :
8
Issue :
3
fYear :
2011
fDate :
7/1/2011 12:00:00 AM
Firstpage :
633
Lastpage :
640
Abstract :
Accurate assessment of the quality of concrete bridge decks and identification of corrosion induced delamination lead to economic management of bridge decks. It has been demonstrated that ground penetrating radar (GPR) can be successfully used for such purposes. The growing demand on GPR has brought into the challenge of developing automatic processes necessary to produce a final accurate interpretation. However, there have been few publications targeting at automatic detection of bridge deck delamination from GPR data. This paper proposes a novel method using partial differential equations to detect rebar (or steel-bar) mat signatures of concrete bridges from GPR data so that the delamination within the bridge deck can be effectively located. The proposed algorithm was tested on both synthetic and real GPR images and the experimental results have demonstrated its accuracy and reliability, even for diminished image contrast and low signal-to-noise ratio. Therefore, an accurate deterioration map of the bridge deck can be generated automatically.
Keywords :
bridges (structures); concrete; condition monitoring; corrosion; delamination; ground penetrating radar; partial differential equations; radar imaging; rebar; steel; GPR data; automatic detection; concrete bridge decks; corrosion induced delamination identification; economic management; ground penetrating radar image; partial differential equations; real GPR images; rebar; signal-to-noise ratio; steel bar; synthetic images; Bridges; Concrete; Delamination; Ground penetrating radar; Inspection; Mathematical model; Signal to noise ratio; Automatic detection; ground penetrating radar (GPR); image processing; partial differential equations (PDEs);
fLanguage :
English
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1545-5955
Type :
jour
DOI :
10.1109/TASE.2010.2092428
Filename :
5672563
Link To Document :
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