Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/2329
Title: GPR Raw-Data Analysis to Detect Crack Using Order Statistic Filtering
Authors: Kilic, Gokhan
Unluturk, Mehmet S.
Keywords: GPR
structural health
crack
Nyquist theorem
neural network
Ground-Penetrating Radar
Infrared Thermography
Concrete
System
Publisher: Amer Soc Testing Materials
Abstract: Ground penetrating radar (GPR) uses data collected with the aid of electromagnetic waves transmitted into a structure by antenna to assess and monitor the structural health of many different kinds of civil infrastructure. With GPR technology promoting their system with promises of the achievement of in excess of 1000 sample points per scan, this research demonstrated on the basis of the Nyquist theorem that 256 sample points per scan provided equally reliable inspection results. Furthermore, 256 sample points per scan GPR data were further analyzed by order statistic filtering with neural networks to locate cracks within concrete materials. The results showed that the neural network order statistic filters are effective in their use of detecting cracks in noisy environments using 256 sample points per scan GPR data.
URI: https://doi.org/10.1520/JTE20150057
https://hdl.handle.net/20.500.14365/2329
ISSN: 0090-3973
1945-7553
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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