• Journal of The Geomorphological Association of Korea
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• v.28 no.3
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• pp.13-26
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• 2021

In mountain gully, channelized debris flow is an important phenomenon in the process of topographical change. Social infrastructure as roads may be damaged by channelized debris flows, but there has been little information about their occurrence and movement to prepare for the risk of the debris flow. Most of the channelized debris flows occur during heavy rains in mountainous valleys that are difficult to access, so there are not many field data. In this study, the topographical characteristics of the catchment, the rainfall and runoff related to the debris flow, the sedimentary pattern and the cross-sectional change of the channel bed, and the underflow velocity of the gravel bed have been investigated and analyzed in the Singi gully where the channelized debris flows occurred. In the catchment, there was almost no sediment runoff because the vegetation combine with the debris landforms and covered the surface. Therefore, the obvious cause of the channelized debris flows is the collapse of the slope and bed of the gully. Even if the gravel, cobbles, and boulders of the channel bed were lost by debris flow, the thalweg change due to debris flow may not be significant because they are supplied from the gully side slope normally. After the gabion structures were installed, the debris flow increased the thalweg change, bed erosion and side slope of the gully. Various sedimentary structures in the gully were classified according to the factors supporting the sedimentation. The hypsometric curve of the gully reflects the debris landforms and vegetation characteristics of the watershed and the sediment runoff due to debris flow, etc. The relationship between the flow velocity and the hydraulic gradient was non-linear under the condition that the porous medium with gully bed gravels is saturated with water. These results may be used as basic data for channelized debris flow research.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• 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 Korean Society of Forest Science
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• v.112 no.4
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• pp.523-529
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• 2023

The aim of this study was to analyze the potential for timber harvesting in the Hongchoen Garisan Leading Forest Management Complex in the national forests, and to suggest an optimal target yield for sustainable timber harvesting. The potential for timber harvesting was assessed by analyzing the area available for timber harvesting using GIS spatial analysis, but excluding areas with a slope of more than 40° (topographical constraints), areas within 30 m on both sides of streams (environmental constraints), and areas more than 300 m away from forest roads (technical constraints). The analysis identified 3,298 ha (49%) of the total complex area of 6,679 ha as available for timber harvesting, yielding a potential harvesting volume of 608,613 m³. In the case of coniferous plantations, the potential harvesting volume was 409,721 m³, which was a very high level that accounted for 67.3% of the total. We also conducted an optimization analysis to minimize the differences in area between age classes, while maintaining sustainable timber harvesting for the next 50 years. An annual average of 41.9 ha (7,988 m³) was determined to be the optimal timber yield, and in this case, it was possible to convert the age class structure to a more stable structure after 50 years.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.530-543
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• 2023

Societal value of the benefits forests provide has grown significantly, given their pivotal role in mitigating climate change and fostering the shift toward a carbon-neutral society. Due to the economic and public value of forests, which extends far beyond landowners and foresters, the forestry sector mutually interacts with society as a whole. Thus, understanding public perceptions and preferences concerning forests and forest policies from the societal viewpoint is vital for shaping future forest policy decisions. This research delved into evolving perceptions over the past 32 years, using a time-series analysis of data gathered from the 'Public awareness survey on forests'. This survey, conducted seven times between 1991 and 2023 by opinion poll agents, provides insights into changing sentiments. The findings reveal a notable increase in public satisfaction with overall forest policies. Specifically, positive sentiments were observed regarding forest rehabilitation, forest trails, education initiatives, and the establishment and functioning of forest recreation facilities. Conversely, the study highlights areas where public satisfaction remained relatively low, notably in matters concerning the use and conversion of mountainous regions, forest disaster prevention, and international forest cooperation. Additionally, the respondents emphasized the need for heightened attention to forest management, the development of forest roads, and increased efforts in overseas afforestation compared to current initiatives.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.61-81
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• 2024

eCall system assists traffic accident victims by connecting emergency rescue institutions with accurate accident information, helping them to identify the on-site situation in the event of a traffic accident. The purpose of this paper is to develop a Korean eCall system that reflects the requirements of domestic emergency rescue institutions and to analyze the expected effects through an integrated demonstration. The results of an integrated demonstration indicated that the communication success rate between the eCall IVS and the call center was 99.25%, and the average location information error was 1.2 m. In particular, it has been confirmed that the average location information error is less than 21.6 meters, as assessed by the Korea Communications Commission when evaluating the accuracy of domestic emergency rescue location information. When the eCall system was introduced, it was confirmed that the time from traffic accidents to hospital arrival could be shortened by 3 m 38 s for highways and 1 m 22 s for general roads. By it to traffic deaths from 2005 to 2022, it was analyzed that the number of fatalities decreased by 82,662, resulting in a reduction of approximately social costs.

• 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 Society of Disaster Information
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• v.20 no.2
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• pp.351-368
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• 2024

Purpose: In general, traffic accidents occur sporadically, so there are various limitations in terms of time and cost when conducting field investigations to prepare prevention and prevention measures. In particular, with the transition to a non-face-to-face society after the COVID-19, there is a greater need to prepare a replacement for field surveys. Therefore, in this study, Roadview provided by various websites was used as an alternative to field surveys in Sasang-gu, Busan City. The possibility was evaluated. Method: The research method was to extract vulnerable points for traffic accidents that occurred between 2016 and 22 and analyze road views based on the field survey evaluation items provided in the Traffic Safety Diagnosis Guidelines. Result: The main result was that Sasang-gu was most vulnerable to accidents involving elderly pedestrians at Sasang-ro, Daedong-ro, and Hakjang-ro. As a result of a detailed analysis of vulnerable points through Road View, Sasang-ro needed control of commercial vehicles and protection of the transportation vulnerable in the market commercial area. Daedong-ro was vulnerable to illegal on-street parking and slope merging sections, and Hakjang-ro was vulnerable to roads that were prone to speeding. When evaluating the possibility of replacing Roadview's field survey based on the results of this analysis, Roadview was able to effectively evaluate most items, such as separation of sidewalks and the location and spacing of safety facilities. However, there were limitations in items such as actual measurement performance. Conclusion: In other words, the road view can replace most field surveys, and the actual measurement evaluation items can be judged to be useful as auxiliary data, resulting in time and cost savings and high efficiency.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.63-73
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• 2024

Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research.