Abstract
Abstract With the advances of computational science and technology, developing ground-penetrating radar (GPR) data processing algorithms that can automate structural defects detection and identification have become an active research subject. This paper focuses on detecting and identifying three major defects in concrete structure, including delamination, air void and moisture through characterizing their reflection signal’s polarity and image shape patterns. To narrow down data scope and leverage data processing efficiency, the analysis starts with the row variance calculation to identify the singular regions. To examine the polarity response, multiple measures including histogram equalization, binarization, derivation and polarity assessment are taken. For shape pattern identification, F-K migration, image binarization and principal signal extraction are performed. To evaluate the algorithms performance, both the simulation data and field test data are utilized with a 900 MHz GPR. The experimental results demonstrate the effectiveness algorithms in identifying and characterizing three major defects in the concrete structure.
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