• International Journal of Highway Engineering
• /
• v.18 no.2
• /
• pp.37-42
• /
• 2016

PURPOSES : The objective of this study is to determine the optimal frequency of ground penetrating radar (GPR) testing for detecting the voids under the pavement. METHODS : In order to determine the optimal frequency of GPR testing for void detection, a full-scale test section was constructed to simulate the actual size of voids under the pavement. Voids of various sizes were created by inserting styrofoam at varying depths under the pavement. Subsequently, 250-, 500-, and 800-MHz ground-coupled GPR testing was conducted in the test section and the resulting GPR signals were recorded. The change in the amplitude of these signals was evaluated by varying the GPR frequency, void size, and void depth. The optimum frequency was determined from the amplitude of the signals. RESULTS: The capacity of GPR to detect voids under the pavement was evaluated by using three different ground-coupled GPR frequencies. In the case of the B-scan GPR data, a parabolic shape occurred in the vicinity of the voids. The maximum GPR amplitude in the A-scan data was used to quantitatively determine the void-detection capacity. CONCLUSIONS: The 250-MHz GPR testing enabled the detection of 10 out of 12 simulated voids, whereas the 500-MHz testing allowed the detection of only five. Furthermore, the amplitude of GPR detection associated with 250-MHz testing is significantly higher than that of 500-MHz testing. This indicates that 250-MHz GPR testing is well-suited for the detection of voids located at depths ranging from 0.5~2.0 m. Testing at frequencies lower than 250 MHz is recommended for void detection at depths greater than 2 m.

• International Journal of Highway Engineering
• /
• v.19 no.5
• /
• pp.69-75
• /
• 2017

PURPOSES : The purpose of this study is to evaluate different types of Ground Penetrating Radar (GPR) testing for characterizing the road cavity detection. The impulse and step-frequency-type GPR tests were conducted on a full-scale testbed with an artificial void installation. After analyzing the response signals of GPR tests for detecting the road cavity, the characteristics of each GPR response was evaluated for a suitable selection of GPR tests. METHODS : Two different types of GPR tests were performed to estimate the limitation and accuracy for detecting the cavities underneath the asphalt pavement. The GPR signal responses were obtained from the testbed with different cavity sizes and depths. The detection limitation was identified by a signal penetration depth at a given cavity for impulse and step-frequency-type GPR testing. The unique signal characteristics was also observed at cavity sections. RESULTS : The impulse-type GPR detected the 500-mm length of cavity at a depth of 1.0 m, and the step-frequency-type GPR detected the cavity up to 1.5 m. This indicates that the detection capacity of the step-frequency type is better than the impulse type. The step-frequency GPR testing also can reflect the howling phenomena that can more accurately determine the cavity. CONCLUSIONS :It is found from this study that the step-frequency GPR testing is more suitable for the road cavity detection of asphalt pavement. The use of step-frequency GPR testing shows a distinct image at the cavity occurrences.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.20 no.4
• /
• pp.104-110
• /
• 2016

To get the empirical data of GPR detection and to develop the image prosessing program of GPR detection data, GPR detection were proceed by the underground pipes and cavities buried in the Chamber. In the case of non pavement and asphalt pavement, water filled cavity that was buried in 0.7m depth was able to detection. But in the case of 1.0 m and 1.3 m buring depth, water filled cavity was not able to detection. In the case of non-reinforced and reinforced concrete pavement, it was difficult to detect the cavity caused by signal interference. GPRiPP programs was developed for image processing of the GPR detection data. The major processing algorithm were background removal, stacking and gain function. With proper image processing of gain function and background removal in GPRiPP program, it was showed that similar results can be obtained with conventional image processing program.

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

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.14 no.6
• /
• pp.616-624
• /
• 2003

The exhaustion of our water resource due to the leakage of waterworks renders it urgent to detect water leakage effectively. In the paper, the detection of water leakage makes use of a pound-penetrating radar(GPR). The region of water leakage is implemented by an acryl box filled with methanol, and then the scale-down experiments are performed by using the GPR system developed in our laboratory. The validity of GPR experiments is assured by showing that the measured data agree well with those finite-difference time-domain(FDTD) simulated results in the same situation. The feasibility of GPR system for the detection of water leakage is investigated by displaying B-scan images according to the distribution of water leakage.

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

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.171-184
• /
• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.211-228
• /
• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.4
• /
• pp.341-353
• /
• 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 Korea institute for structural maintenance and inspection
• /
• v.20 no.2
• /
• pp.86-93
• /
• 2016

To find the buried pipes and cavities, GPR detection were proceed by the type and depth of underground pipes and cavities buried in the Chamber. In the case of asphalt pavement and non-pavement, the exploration of buried pipe were easy than the concrete and reinforced concrete pavement. In the case of air cavity, the buried depth of 1 m was evaluated as the detection was possible.