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, G.
Unluturk, M.S.
Keywords: Crack
Gpr
Neural Network
Nyquist Theorem
Structural Health
Publisher: ASTM International
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. Copyright © 2014 by ASTM.
URI: https://doi.org/10.1520/JTE20150057
ISSN: 0090-3973
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record



CORE Recommender

WEB OF SCIENCETM
Citations

1
checked on Mar 26, 2025

Page view(s)

108
checked on Mar 31, 2025

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.