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gpr Raw-data Order Statistic Filtering and Split-Spectrum processing to Detect Moisture

REMOTE SENSING 2014年第6期6卷 4687-4704页

作者： Kilic, Gokhan Izmir Univ Econ Dept Civil Engn TR-35330 Izmir Turkey

Considerable research into the area of bridge health monitoring has been undertaken;however, information is still lacking on the effects of certain defects, such as moisture ingress, on the results of ground penetrating radar (gpr) surveying. In this paper, this issue will be addressed by examining the results of a gpr bridge survey, specifically the effect of moisture in the predicted position of the rebars. It was found that moisture ingress alters the radargram to indicate distortion or skewing of the steel reinforcements, when in fact destructive testing was able to confirm that no such distortion or skewing had occurred. Additionally, split-spectrum processing with order statistic filters was utilized to detect moisture ingress from the gpr raw data.

关键词： gpr and data processing bridge structures structures non-destructive moisture ingress split-spectrum processing (SSP) order statistic filters

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Performance evaluation of the neural networks for moisture detection using gpr

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NONDESTRUCTIVE TESTING AND EVALUATION 2014年第4期29卷 283-296页

作者： Kilic, Gokhan Unluturk, Mehmet S. Izmir Univ Econ Dept Civil Engn TR-35330 Izmir Turkey Izmir Univ Econ Dept Software Engn TR-35330 Izmir Turkey

Ground penetrating radar (gpr) is a highly researched area;however, despite this, there is a lack of knowledge about the well-known problem of moisture distorting the results of gpr surveys. This research analyses the results of a gpr survey on a Case Study Bridge structure in order to analyse this effect, specifically when checking for the positioning of rebar. The expected distortions of the gpr results due to the presence of moisture were indeed present, as further evidenced by subsequent destructive testing and velocity analysis. Furthermore, neural networks were also utilised to detect moisture ingress from the gpr raw data.

关键词： gpr and data processing bridge structures non-destructive moisture ingress neural networks back-propagation learning algorithm

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Applications of Ground-Penetrating Radar (gpr) to Detect Hidden Beam Positions

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JOURNAL OF TESTING AND EVALUATION 2017年第3期45卷 911-921页

作者： Kilic, Gokhan Izmir Univ Econ Dept Civil Engn Izmir Turkey

Ground-penetrating radar (gpr) uses electromagnetic waves to investigate the structures. In this investigation method, an electromagnetic wave is transmitted using an antenna and the received signal is recorded. Detection of beam positions in this gpr data requires the skills of a trained human operator. This study utilized a multi-layer neural network to detect beam positions in the gpr data. The visual description and definition of gpr data has major disadvantages and a neural network has been studied to overcome these shortcomings. A set of 32,740 training vectors with a length of 64 data was implemented to train the neural network. A new set of 16,370 testing vectors with a length of 64 data was then prepared to test the performance. Testing results suggest that the neural network is promising methods for the detection of beam positions in the gpr data.

关键词： backpropagation learning algorithm Bayes optimal decision rule Gram-Charlier series gpr and data processing neural network

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