• Geomechanics and Engineering
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• v.37 no.5
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• pp.467-474
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• 2024

Monitoring and managing the condition of underground utilities is crucial for ground stability. This study aims to determine whether images obtained using ground penetrating radar (GPR) accurately reflect the characteristics of buried pipelines through image analysis. The investigation focuses on pipelines made from different materials, namely concrete and steel, with concrete pipes tested under various diameters to assess detectability under differing conditions. A total of 400 images are acquired at locations with pipelines, and for comparison, an additional 100 data points are collected from areas without pipelines. The study employs GPR at frequencies of 200 MHz and 600 MHz, and image analysis is performed using machine learning-based convolutional neural network (CNN) techniques. The analysis results demonstrate high classification reliability based on the training data, especially in distinguishing between pipes of the same material but of different diameters. The findings suggest that the integration of GPR and CNN algorithms can offer satisfactory performance in exploring the ground's interior characteristics.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.11
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• pp.101-106
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• 2005

A ground penetrating image radar (GPR) using an ultra wideband (UWB)impulse waveform is developed for non destructive detection of metallic pipelines buried under the ground. Dielectric constant of test field is measured and then a GPR system is designed for better detection up to 1 meter deep. By considering total path loss, volume of complete system, and resolution, upper and lower frequencies are chosen. First, a UWB impulse for the frequency bandwidth of the impulse is chosen with rising time less than 1 ns, and then compact planar UWB dipole antenna suitable for frequency bandwidth of a UWB impulse is designed. Also, to receive reflected signals, a digital storage oscilloscope is used. For measurement, a monostatic technique and a migration technique are used. For visualizing underground targets, simple image processing techniques of A-scan removal and B-scan average removal are applied. The prototype of the system is tested on a test field in wet clay soil and it is shown that the developed system has a good ability in detecting underground metal objects, even small targets of several centimeters.

• Corrosion Science and Technology
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• v.3 no.6
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• pp.262-264
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• 2004

Ground Penetrating Radar (GPR) has been used to image inside concrete specimens embedded with steel bars and delamination. An imaging algorithm has been developed to improve measurement output generated from a commercial radar system. For the experiments, laboratory size concrete specimens are made with the dimensions of $1,000mm(W){\times}1,000mm(L){\times}250mm(D)$. The results have shown improved output of the radar measurements compared to commercially available processing methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.3-12
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• 2016

This paper describes the development of the ground penetrating radar (GPR) system using UWB impulse radar with 48 Channel array. GPR is an effective alternative technology to resolve th disadvantages of metal detectors. Metal detectors have a very low detection probability of non-metallic landmine and high false alarm rates caused by metallic materials under the ground. In this paper, we use the mono-cycle pulse waveform with about 600 ps pulse width to obtain high resolution landmine microwave images. In order to analyze performances of this system, we utilize indoor test facility that made up of rough sandy loam which representative Korean soil. The mimic landmine models of metal/non-metal and anti-tank/anti-personnel landmines buried in DMZ (demilitarized zone) of Korea are used to analyze the detection depth and the shape of the mines using microwave image.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2342-2344
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• 2005

초광대역 임펄스를 이용한 비파괴 지중 매설물 탐지용 지반 탐사 레이더(Ground penetrating image radar: GPR)를 개발하였다. 최대 탐사 깊이를 고려하여, 900 picosecond(ps) 상승 시간을 갖는 초광대역 임펄스를 설계하였고, 임펄스 발생기의 주파수 특성을 고려하여, 소형 평판형 다이폴 안테나가 설계되었다. 또한, 지중으로부터 반사되는 신호를 수신하기 위해서 고속의 A/D를 사용하였다. 측정은 송수신 안테나의 간격을 고정한 Bistatic 방식을 사용하였으며, 지중 매설물의 영상처리 판별을 위해 마이그레이션(migration) 기법을 사용하였다. 개발된 시스템은 금속 물체와 비금속 물체가 매설된 실증 시험장에서 시험되었고, 평면 해상도 및 깊이에 대한 해상도가 우수함을 보였다.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.61-67
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• 2016

OBJECTIVES : The objective of this study is to detect road cavities using multi-channel 3D ground penetrating radar (GPR) tests owned by the Seoul Metropolitan Government. METHODS : Ground-penetrating radar tests were conducted on 204 road-cavity test sections, and the GPR signal patterns were analyzed to classify signal shape, amplitude, and phase change. RESULTS : The shapes of the GPR signals of road-cavity sections were circular or ellipsoidal in the plane image of the 3D GPR results. However, in the longitudinal or transverse direction, the signals showed mostly unsymmetrical (or symmetrical in some cases) parabolic shapes. The amplitude of the GPR signals reflected from road cavities was stronger than that from other media. No particular pattern of the amplitude was found because of nonuniform medium and utilities nearby. In many cases where road cavities extended to the bottom of the asphalt concrete layer, the signal phase was reversed. However, no reversed signal was found in subbase, subgrade, or deeper locations. CONCLUSIONS : For detecting road cavities, the results of the GPR signal-pattern analysis can be applied. In general, GPR signals on road cavity-sections had unsymmetrical hyperbolic shape, relatively stronger amplitude, and reversed phase. Owing to the uncertainties of underground materials, utilities, and road cavities, GPR signal interpretation was difficult. To perform quantitative analysis for road cavity detection, additional GPR tests and signal pattern analysis need to be conducted.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.2
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• pp.177-184
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• 2003

A series of experimental work has been conducted to evaluate the capability of Ground Penetrating Radar (GPR) system in detecting delamination inside concrete. Three antenna at 900 MHz, 1000 MHz, and 1500 MHz frequency are used in the experiments for laboratory size specimens, and 400 MHz antenna has been used for a large size specimen. The laboratory size specimens have the dimensions of 1,000 mm (length) ${\times}$ 600 mm (width) ${\times}$ 140 mm (thickness) with a delamination of 200 mm (length) ${\times}$ 600 mm (width) ${\times}$ 140 mm (thickness). The cover depth of the delamination is varied as follows: 20 mm, 30 mm, 60 mm, and 70 mm. In all cases, the delamination has been successfully identified. The property of three frequencies was seized about detecting delamination. Also, it was shown that the image results in GPR were improved by signal processing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.565-574
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• 2000

A new ground penetrating radar imaging method for the estimation of buried artificial structures location and their approximate shapes in dispersive lossy ground is investigated. Fundamental idea is based on estimating delayed time and amplitude retrieval coefficients from scattered signals by buried scatterers. Using absolute value integration of each scanning site not only improve the accuracy of measured scattered signal, but also offers convenient ways to extract the image of buried structures. Multi-term Debye model was employed to describe a dispersive and lossy ground medium. We used the finite difference time domain method to discretize the wave equation in continuous form into the machine suitable form. This imaging method uses a new wave path tracing technique in time domain, which is helpful to identify the exact position of buried structures against the ground surface fluctuations.

• Geophysics and Geophysical Exploration
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• v.5 no.3
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• pp.185-192
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• 2002

We made a study of 3-D migration which could precisely image data of GPR (Ground Penetrating Radar) applied to NDT (Non-Destructive Test) field for the inspection of structural safety. In this study, we obtained 3-D migrated images of important targets in structuresurvey (e.g. steel pipes, cracks) by using 3-D Kirchhoff prestack depth migration scheme developed for seismic data processing. For a concrete model consisting of steel pipe and void, the targets have been well defined with opposite amplitude according to the parameters of the targets. And migrated images using Parallel-Broadside array (XX configuration) have shown higher resolution than those using Perpendicular-Broadside array (YY configuration) when steel pipes had different sizes. Therefore, it is required to analyze the migrated image of XX configuration as well as that of general YY configuration in order to get more accurate information. As the last stage, we chose a model including two steel pipes which cross each other. The upper pipe has been resolved clearly but the lower has been imaged bigger than the model size due to the high conductivity of the upper steel.