• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.11
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• pp.42-47
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• 2007

In this paper, influence of lossy ground and gap variation between lossy ground and UWB antenna on impulse propagation in time domain for impulse ground penetrating radar (GPR) is numerically and experimentally investigated. For this study, a novel planar UWB fat dipole antenna is developed. First, influence of lossy ground and gap variation between lossy ground and UWB antenna is simulated. For verification, a test field of sand and wet clay soil is built and using the developed dipole antenna, transmission behavior is investigated at the test field. With an aid of IDFT (inverse discrete Fourier transform), time domain impulse response for transmission coefficient measured and simulated in frequency domain is obtained. Measurement and simulation show that the frequency of maximum transmission coefficient and transmission coefficient are increased with higher dielectric constant and larger gap distance. In time domain, it is shown that for higher dielectric constant, the amplitude of the received signal in time domain is higher and reflected signals are seriously modified. Also, it is found that variation of gap between antenna and ground surface makes timing of peak value changed.

• Journal of the Korean Geotechnical Society
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• v.34 no.11
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• pp.93-105
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• 2018

Precise investigation and interpretation of the ground subsidence risk factors needed to predict and evaluate the settlement problems of the surrounding ground due to the ground excavation. There are various geophysical exploration methods to investigate the ground subsidence risk factors. However, there are factors that influence the characteristics of the underground medium in these geophysical methods, and the actual soil contains complex factors affecting geophysical exploration. Therefore, it is necessary to analyze the effects on the geophysical methods based on the understanding of the geotechnical properties of soil. In this study, a test bed was constructed to consider various complicated factors in the complex ground and the ground behavior was analyzed by numerical analysis. In addition, we analyzed the limitations on investigating the ground subsidence risk factors through ground penetration radar (GPR) survey. As a result, ground subsidence of Open-cut Type Excavation is caused by various factors. Especially, in the case of soft ground condition, it was found that it was greatly influenced by the flow change of groundwater level. At the center frequency of GPR of 250 MHz, the attenuation of the electromagnetic wave is severely attenuated in the clay with high electrical conductivity, making it difficult to penetrate deeply into the ground (4 m below the surface). As the electromagnetic waves pass through the groundwater level below the groundwater, the attenuation of the electromagnetic waves becomes severe.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.27-37
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• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• Journal of the Korean Geotechnical Society
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• v.37 no.9
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• pp.13-24
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• 2021

Recently, attempts have been made to detect fouling patterns in the ground using Ground Penetrating Radar (GPR) during the maintenance of gravel ballast railway tracks. However, dealing with GPR signal data obtained with a large amount of noise in a site where complex ground conditions are mixed, often depends on the experience of experts, and there are many difficulties in precise analysis. Therefore, in this study, a numerical modeling technique that can quantitatively simulate the GPR signal characteristics according to the degree of fouling of the gravel ballast material was proposed using python-based open-source code gprMax and RSA (Random sequential Absorption) algorithm. To confirm the accuracy of the simulation model, model tests were manufactured and the results were compared to each other. In addition, the identification of the fouling layer in the model test and analysis by various test conditions was evaluated and the results were analyzed.

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.2
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• pp.177-184
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• 2017

Many ground subsidence accidents have happened in Korea. The accident was caused by the subsidence and leakage of the deteriorated sewage pipe. This study aims to establish the empirical data of the ground penetration radar(GPR) detection for ground subsidence. A test bed was also manufactured for the same purpose. The GPR detection variables are embedment depth and horizontal distance of embedded cast iron pipe and expanded polystyrene(EPS). From the detection results, the EPS embedded by a depth of 1.5m was difficult for detection. The EPS closely embedded to the cast iron pipe within a 0.5m distance had a very strong cast iron pipe signal. Therefore, the detection was impossible. This study developed an image processing program, called the GPR image processing program(GPRiPP), to process the GPR detection results. Its major function is the gain function, which amplifies the wiggle wave signal. Compared to the existing programs, the GPRiPP is capable of showing a similar image processing performance.

• Geophysics and Geophysical Exploration
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• v.19 no.3
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• pp.153-163
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• 2016

In order to prevent ground collapses by groundwater level drawdown, we need to understand the groundwater flow and also make an analytical approach to the cause of the collapses. In this study, we used the result of the soil lab tests to compare and review the suitability of the various interaction equations about the relation between volumetric water content and the dielectric constant. In addition, using GPR (Ground-Penetrating Radar), we reviewed the possibility of calculating an estimate of dielectric constant. Lastly, we applied seepage analysis and stress-strain analysis to the sandy ground given by ground excavation. In comparison with the previous result of the soil lab tests, we similarly predicted the suction of unsaturated soil from results of stress-strain analysis considered the seepage force for the unsaturated soil.

• Journal of the Korean GEO-environmental Society
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• v.19 no.8
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• pp.11-17
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• 2018

Recently, road subsidence has been increasing in urban areas, threatening the safety of citizens. In the lower part of the road, various road facilities such as water supply and drainage pipelines and telecommunication facilities are buried, and the deterioration of the facilities causes the road subsidence. Especially, in the case of old sewer which are attracting attention as a main cause of ground subsidence, the risk of subsidence is calculated indirectly through CCTV exploration. Currently, we are finding cavity through GPR exploration. However, it is difficult to find the sewer back cavity because it is explored from the surface of the road. Thus, the nondestructive cavity exploration techniques was investigated in this study and we confirmed the applicability through experiments on the test-bed. In this study a new quantitative method is proposed to detect the cavity around sewer.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.49-60
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• 2024

Ground-penetrating radar (GPR) enables rapid data acquisition over extensive areas, but interpreting the obtained data requires specialized knowledge. Numerous studies have utilized numerical analysis methods to examine GPR signal characteristics under various conditions. To develop more realistic numerical models, the heterogeneous nature of the ground, which causes clutter, must be considered. Clutter refers to signals reflected by objects other than the target. The Peplinski material model and fractal techniques can simulate these heterogeneous characteristics, yet there is a shortage of research on the necessary input parameters. Moreover, methods for quantitatively evaluating the similarity between field and analytical data are not well established. In this study, we calculated the autocorrelation coefficient of field data and determined the correlation length using the autocorrelation function. The correlation length represented the temporal or spatial distance over which data exhibited similarity. By comparing the correlation length of field data with that of the numerical model incorporating fractal weights, we quantitatively evaluated a numerical model for heterogeneous ground. Consequently, the results of this study demonstrated a numerical modeling technique that reflected the clutter characteristics of the field through correlation length.