• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.30-38
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• 2023

As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.663-670
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• 2023

In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.115-125
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• 2023

In this study, we evaluated the behavior of frozen soil using an electrical resistivity survey method-a nondestructive technique-and examined its characteristics through field experiments. Frozen soil was artificially prepared by injecting fluid to accelerate the freezing process, and naturally frozen soil was selected in a nearby area for comparison. A dynamic cone penetration test (DCPT) was performed to compare the reliability of the electrical resistivity survey, and time-domain reflectometry surveys were performed to assess the moisture content of the ground. Field experiments were conducted in February-when the atmosphere temperature was below freezing-and May-when the temperature was above freezing. This temperature-compensated method was used to determine reliability because the behavior of frozen soil depends on the underlying temperature. In the resistivity survey method, a section of high electrical resistivity was observed under freezing conditions due to the frozen water and converted into porosity. The converted porosity was compared with the porosity inferred from the DCPT, and the results showed that the measured electrical resistivity was valid.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.1-11
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• 2023

Big tech companies are further strengthening its status against the background of data accumulation, price competitiveness by the platform, and competitive advantage due to the network effect. The competition subcommittee of the European Union(EU) imposed a huge fine on Google for antitrust violations, which was interpreted as an attempt to collect Google's unpaid taxes. In fact, taxation efforts in the form of 'Google tax' are underway, targeting expedient tax avoidance by global platforms. It has power and has a considerable influence on the startup ecosystem. The domestic sales and tax scale of global platforms, which have a great impact on domestic content startups and small and medium-sized venture companies, are not accurately measured. In the case of Google, according to research literature, sales in Korea were estimated at about 2 trillion to 3 trillion won in 2017, but Google Korea reported sales of 290 billion won in 2021 and paid 13 billion won in taxes. This study aims to verify the economic effect of the global platform that has a great influence on Korea, and specifically to quantitatively estimate the annual domestic sales and taxes of Google, a representative global platform. As a result of estimating Google's annual domestic sales and taxes based on the figures presented in the document related to Google's economic effect published by Google, the result was 4 to 9 trillion won in annual sales and 390.6 to 913.1 billion won in taxes. This study is meaningful in that it provides basic data on the direction of national and tax policies in the future digital economy era by estimating the problem of tax authority by country of global platform companies with a specific example of Google.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.77-84
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• 2024

Aging and damaged underground utilities cause cavity and ground subsidence under roads, which can cause economic losses and risk user safety. This study used infrared cameras to assess the thermal characteristics of such cavities and evaluate their reliability using a CNN algorithm. PVC pipes were embedded at various depths in a test site measuring 400 cm × 50 cm × 40 cm. Concrete blocks were used to simulate road surfaces, and measurements were taken from 4 PM to noon the following day. The initial temperatures measured by the infrared camera were 43.7℃, 43.8℃, and 41.9℃, reflecting atmospheric temperature changes during the measurement period. The RP algorithm generates images in four resolutions, i.e., 10,000 × 10,000, 2,000 × 2,000, 1,000 × 1,000, and 100 × 100 pixels. The accuracy of the CNN model using RP images as input was 99%, 97%, 98%, and 96%, respectively. These results represent a considerable improvement over the 73% accuracy obtained using time-series images, with an improvement greater than 20% when using the RP algorithm-based inputs.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.93-100
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• 2024

Pore water freezes in low-temperature compaction, which leads to different compaction characteristics compared to room temperature conditions. In regions like Alberta, Canada, where organic soils are prevalent, compaction performance is influenced by the high water retention and compressibility of organic soils, as well as their sensitivity to freezing and thawing. Alberta's strict environmental regulations demand the reuse of excavated soil for backfill, and the long winter season creates challenging conditions for civil engineering projects. In this study, a laboratory compaction test was conducted to evaluate the low-temperature compaction characteristics of organic soils with varying organic content. The results indicate that the optimum moisture content increases as the organic content increases, and the maximum dry unit weight decreases by up to 21.9%. In addition, under temperature conditions below -4℃, no optimum moisture content was observed, and the dry unit weight decreased as the moisture content increased.

• Journal of the Korean Geosynthetics Society
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• v.23 no.2
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• pp.53-62
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• 2024

Recently, development of non-traditional energy such as oil sands has been actively conducted in the cold region such as Canada. Frozen soil has different thermal and mechanical characteristics from general soil due to its high organic contents. This study evaluated the impact of organic matter content on the thermal and mechanical behavior of frozen soil samples collected from Alberta, Canada, and Gangwon Province, South Korea. As the organic content increases, the maximum dry unit weight decreases and the optimum moisture content increases in compaction tests. In uniaxial compression tests under frozen conditions, the strength of the frozen specimens increased as the temperature decreased. The strength of Canada soil sample increased with higher organic matter content at low temperatures. However, the strength of frozen soil was not significantly affected by organic matter content due to the complex behavior and unfrozen water content. Thermal conductivity tests showed higher thermal conductivity in frozen conditions compared to unfrozen conditions, due to the higher thermal conductivity of ice compared to water. These findings provide essential data for geotechnical design and construction in large-scale projects such as oil sands development in cold regions. Further research is needed to explore the impact of organic matter content on different types of frozen soils.

• Journal of the Korean Geotechnical Society
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• v.40 no.2
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• pp.105-113
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• 2024

Underground buried cables can cause disconnections during the construction of roads and other subterranean structures due to uncertain designs. This paper describes experiments conducted to detect and verify the locations of these cables utilizing ground penetrating radar (GPR). The experiments were carried out at an active road construction site, where cable burial was anticipated. The GPR used operated within a frequency range of 400 MHz to 900 MHz to probe underground structures. The exploration methodology consisted of an initial GPR test to survey the entire area, followed by a secondary test informed by the results of the initial experiment, incorporating a diverse and increased number of lines. The findings confirmed the hyperbolic reflection patterns of cables at consistent locations along the same lines. These patterns were then compared to existing designs to corroborate the presence of cables at the identified locations. This research establishes an effective GPR methodology based on the electromagnetic wave reflection pattern, specifically the hyperbola, to detect difficult-to-locate underground buried cables.