• Journal of the Korean GEO-environmental Society
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• v.22 no.12
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• pp.33-40
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• 2021

The deterioration of underground facilities, disturbance of the ground due to underground development activities, and changes in ground water can cause ground subsidence accidents in the urban areas. The investigation on the geotechnical and hydraulic factors affecting the ground subsidence accident is very significant to predict the ground subsidence risk in advance. In this study, an analysis DB was constructed through 3D ground modeling to utilize the currently operating geotechnical survey information DB and ground water behavior information for risk prediction. Additionally, using these results, the relationship between the actual ground subsidence occurrence history and ground conditions and ground water level changes was confirmed. Furthermore, the methodology used to visualize the risk of ground subsidence was presented by reconstructing the engineering characteristics of the soil presented according to the Unified Soil Classification System (USCS) in the existing geotechnical survey information into the internal erosion sensitivity of the soil, Based on the result, it was confirmed that the ground in the area where the ground subsidence occurred consists of more than 40% of sand (SM, SC, SP, SW) vulnerable to internal erosion. In addition, the effect of the occurrence frequency of ground subsidence due to the change in ground water level is also confirmed.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.175-201
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• 2023

In the midst of going through a non-face-to-face society, most of human activities narrowed down to the platform, restrictions on external activities are bringing the internal scalability of digital technology. Metaverse is virtually shifting reality and increasing the possibility of utilization in various areas. However, researches linked to the educational effects of metaverse, especially students with disabilities, are still an unknown area that lacks exploration. This paper focuses on the fact that metaverse-education is widening educational fields that meets the various needs of disabled students to realize social good and inclusive education, and communication effects such as resolving barriers to interaction are prominent. As a research method, examining literature research papers linked to AR/VR, metaverse with communication skills, interviews, articles, and columns by experts, and policy suggestions and implications for the special education was conducted. Although the limitations of research are confirmed, significant results are found on inclusive education, which provides educational maximizing effects and realizing human rights through direct immersive experience reflecting the Cone of Experience Theory. Hopefully follow-up studies on meta-edu for disabled students will be carried out in the future, and various interdisciplinary discussions are needed to carefully observe inclusive policies and benefits so that the socially vulnerable are not excluded from technologies in ICT society.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Environmental Health Sciences
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• v.49 no.4
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• pp.218-227
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• 2023

Background: Since 2019, the Ministry of Environment has implemented a seasonal fine dust management system from December to March, targeting high PM_2.5 levels with the aim of reducing PM_2.5 concentrations and protecting public health. The focus of improving the seasonal management system lies in the atmospheric PM_2.5 levels. Considering the primary goal of protecting public health, it is necessary to analyze the policy effects from an exposure perspective rather than a concentration-based approach. Objectives: This study aims to quantitatively assess the improvement of indoor PM_2.5 levels and the health impacts of the seasonal management system by comparing the periods before and during its implementation in residential environments. Methods: PM_2.5 concentrations within residential environments in a metropolitan area were measured using an optical particle counter (IAQ-C7, K-weather, Ltd, Korea) at one-minute intervals during the pre-implementation period (November 21~25, 2022) and during the implementation period (December 19~23, 2022). Based on the measured PM_2.5 concentrations, a quantitative evaluation of cancer and mortality risks was conducted according to age and gender. Results: The results of comparing indoor and outdoor PM_2.5 concentrations before and during the implementation of the seasonal management system showed a decrease of approximately 56.6% and 47.9%, respectively. Health risk assessments revealed that both the safety-limit-based and safety-target-based Hazard Quotients (HQ) exceeded the threshold of 0.1 for children under 19 years of age, both before and after the implementation. The mortality risk decreased by approximately 47.9% after the implementation, with children aged 0-9 showing the highest mortality risk at 0.9%. Conclusions: The findings of this study confirmed the positive health impacts of the seasonal management system across all age groups, particularly children under 19 who are more vulnerable to fine dust exposure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.601-610
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• 2023

The comprehension of water-level behavior in rivers is essential for effective flood and river environmental management. The objective of this study is to propose a methodology that can be used by field engineers engaged in actual practice, to readily identify the characteristics of water-level behavior during flood events. To this end, a total of 45 historical water-level records from 2010 to 2022 year, which provide flood information for the flood vulnerable districts of the Han River, were obtained. A Water-level Rise Behavior Curve (WRBC) was developed and suggested to quantify the amount of water-level rise per unit time during flood. As a result, the water-level rises by more than 80% of the total rise within the first 6.2 hours, followed by a gradual rise. The time required to achieve a particular equilibrium varied depending on the area and runoff characteristics of the upstream. Furthermore, the study revealed that the WRBC provides a statistical representation of the water-level rise trend during floods, and can be effectively utilized for flood mitigation measures in waterfront spaces and irrigation facilities.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Journal of Industrial Convergence
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• v.21 no.7
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• pp.29-39
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• 2023

In the recent period, there have been numerous earthquakes both domestically and internationally, and buildings in South Korea are particularly vulnerable to seismic design and earthquake damage. Therefore, the objective of this study is to discover an effective method for assessing the seismic vulnerability of buildings and conducting a density analysis of high-risk structures. The aim is to model this approach and validate it using data from pilot area(Seoul). To achieve this, two modeling techniques were employed, of which the predictive accuracy of the statistical analysis technique was 87%. Among the machine learning techniques, Random Forest Model exhibited the highest predictive accuracy, and the accuracy of the model on the Test Set was determined to be 97.1%. As a result of the analysis, the district rating revealed that Gwangjin-gu and Songpa-gu were relatively at higher risk, and the density analysis of at-risk buildings predicted that Seocho-gu, Gwanak-gu, and Gangseo-gu were relatively at higher risk. Finally, the result of the statistical analysis technique was predicted as more dangerous than those of the machine learning technique. However, considering that about 18.9% of the buildings in Seoul are designed to withstand the Seismic intensity of 6.5 (MMI), which is the standard for seismic-resistant design in South Korea, the result of the machine learning technique was predicted to be more accurate. The current research is limited in that it only considers buildings without taking into account factors such as population density, police stations, and fire stations. Considering these limitations in future studies would lead to more comprehensive and valuable research.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.70-83
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• 2023

Recently, the Korea Forest Service has implemented a planning project about wind corridor forests as a response measure to climate change. Based on this, research on wind corridors has been underway. For the creation of wind corridor forests, a preliminary evaluation of the wind corridor function is necessary. However, currently, there is no evaluation index to directly evaluate and spatially distinguish the types of wind corridors, and analysis is being performed based on indirect indicators. Therefore, this study proposed a method to evaluate and classify wind corridors by utilizing heat deficit analysis as an evaluation index for cold air generation. Heat deficit was analyzed using a cold air analysis model called Kaltluftabflussmodell_21 (KLAM_21). According to the results of the simulation analysis, the wind path was functionally classified. The top 5% were classified as cold-air generating Areas (CGA), and the bottom 5% as cold-air vulnerable Areas (CVA). In addition, the cold-air flowing Areas (CFA) were classified by identifying the flow of cold air moving from the cold air generation area. It is expected that the methodology of this study can be utilized as an evaluation method for the effectiveness of wind corridors. It is also anticipated to be used as an evaluation index to be presented in the selection of wind corridor forest sites.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.49-56
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• 2023

Impact based forecast refers to providing information on potential socioeconomic risks according to weather conditions, away from the existing weather factor-oriented forecast. Developed weather countries are investing manpower and finances in technology development to provide and spread impact information, but awareness of impact based forecasts has not spread in Korea. In addition, the focus is on disasters such as floods and typhoons, which cause a lot of damage to impact based forecasts, and research on evaluating the impact of vehicle risks due to strong winds in the transportation sector with relatively low damage is insufficient. In Korea, there are not many cases of damage to vehicle conduction caused by strong winds, but there are cases of damage and the need for research is increasing. Road vulnerability is required to evaluate the risk of vehicles caused by strong winds, and the purpose of this study was to calculate the criteria for road vulnerability. The road vulnerability evaluation was evaluated by the altitude of the road, the number of lanes, the type of road. As a result of the analysis, it was found that the vulnerable area was well reproduced. It is judged that the results of this study can be used as a criterion for preparing an objective evaluation of potential risks for vehicle drivers.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.24.1-24.15
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• 2023

Background: The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. Methods: We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Results: Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. Conclusion: The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.