• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.1
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• pp.61-67
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• 2018

The effects of subsurface inhomogeneities on the detection of buried metal pipes in ground-penetrating radar(GPR) signals are investigated numerically. To model the electrical properties of the subsurface inhomogeneities, the continuous random media(CRM) generation technique is introduced. For the electromagnetic simulation of GPR signals, the finite-difference time-domain(FDTD) method is implemented. As a function of the standard deviation and the correlation length of the relative permittivity distribution for a randomly inhomogeneous ground, the GPR signals of the buried metal pipes are compared using numerical simulations. As the subsurface inhomogeneities increase, the GPR signals of the buried pipes are distorted because of the effect of the subsurface clutter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.399-406
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• 2019

The variation of ground-penetrating radar(GPR) signal characteristics from dielectric-filled nonmetallic pipes buried in inhomogeneous ground are compared through a numerical simulation. The relative permittivity distribution of the ground is generated by using the continuous random media(CRM) technique. As a function of the relative permittivity of the material filling the nonmetallic pipe buried in the ground media, GPR signals are simulated by using the finite-difference time-domain(FDTD) method. We show that, unlike the case for homogeneous ground, the distortion characteristics of the reflected waves caused by the front convex surface and the rear concave surface of the pipe buried in inhomogeneous ground are different depending on the permittivity contrast between the inside and outside of the pipe.

• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.2
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• pp.167-174
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• 2000

Ground penetrating radar(GPR) was applied for measuring depths, sizes and intervals of rebars embedded in concrete. A concrete wall was constructed for this study and a sand pool and a concrete block were used for simulation. Result of this study shows that GPR can be used for measuring rebar depths and intervals, even though it is limitary, but that measuring sizes is almost impossible. Simulation with the sand pool was helpful for research on the versatile rebar arrays though signal was not clear as real concrete wall. A concrete block with many cylindrical holes for inserting different sized rebars could not be used for simulator due to many unknown reflective waves. Antenna orientation must be perpendicular to rebars for large reflection signal.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• 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.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.30-33
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• 2001

This paper discusses the system design of a synthetic aperture radar system based on a pulse-echo radar. The design consists of an ultra-wide bandwidth antenna, an amplitude modulation, timing stabilities, and high speed a/d conversions with an equivalent-time sampling. Experiment results show that GPR(Ground Penetrating Radar) can be used to explore buried electric facilities.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.119-128
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• 2005

The assessment of erosional and depositional patterns near bridge piers is essential to understand the fluvial scour process. Geophysical surveys are particularly effective in determining the riverbed variations in a river and may also be of value for obtaining the previous scour history below the riverbed profile. In this study, GPR (Ground Penetrating Radar), as a non-destructive geophysical technique, was used to assess the existence and depth of existing and infilled scour thickness, streambed materials, and pre- and post- scour surfaces at the bridge piers in Han River, June 2002 and October 2002. The GPR acquisition system used for obtaining profiles of the shallow subsurface deposits was a portable GSSI SIR 2000 system with 100 and 400 MHz antennas. The GPR data obtained along the 24 bridge piers in the flow direction of the river and in the surroundings of 5 bridge piers were compared and presented in this study. It is concluded that GPR surveys can be effective in determining both the water depth and sub-bottom geological structure near the bridge piers and abutments provided that the appropriate instrumentation and operational procedures are applied.

• 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 Korea Concrete Institute Conference
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• 1998.10b
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• pp.785-790
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• 1998

Ground Penetrating Radar (GPR) is a powerful tool with a wide range of applications in the nondestructive testing of concrete. It's useful for the detection of steel bars and delaminations embedded inside concrete, nondestructively. The purpose of this study is to detect a reinforced bar embedded inside concrete and to determine the range of application using GPR. A concrete specimen used for this study has a 25mm diameter steel bar and it's dimensions are 1,000 mm (L)× 1,000 mm(W)×280 mm(D). The advantages and limitations of GPR in these applications for concrete are also discussed.