A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images

Author:

Asadi Pouria,Gindy Mayrai,Alvarez Marco

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Reference21 articles.

1. AASHTO LRFD (2012). AASHTO LRFD bridge design specifications: Customary US units, American Association of State Highway and Transportation Officials, Washington, D.C., USA.

2. Asadi, P. and Gindy, M. (2019). “DECKGPRHvl.0 dataset.” https://github.com/PouriaAI/GPR-Detection/ [Accessed on January 10, 2019].

3. ASTM D6087 (2008). Standard test method for evaluating asphalt-covered concrete bridge decks using ground penetrating radar, D6087, ASTM International, West Conshohocken, PA, USA.

4. Blum, A. (1992). Neural networks in C++: An object-oriented framework for building connectionist systems, John Wiley & Sons, New York, NY, USA.

5. Bouzerdoum, A., Tivive, F. H. C., and Abeynayake, C. (2016). “Target detection in GPR data using joint low-rank and sparsity constraints.” Compressive Sensing V: From Diverse Modalities to Big Data Analytics. International Society for Optics and Photonics, Vol. 9857, p. 98570A, DOI: https://doi.org/10.1117/12.2228345 .

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