• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.570-577
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• 2019

Purpose: The purpose of this study is to develop a system that enables quick on-site response using real-time decision-making by sharing the results of measurement and management performed in the field for safe temporary construction. Method: It is possible to take preemptive responses during construction by identifying the safety factors of construction conditions from measurement results and determining the risk factors such as soil properties and variability of climate change that can occur during construction by simultaneously using the back analysis method reflected in the measurement system and structural review. Result: we developed a back analysis algorithm of the SUNEX program to cope with the discrepancies between the design results and measured results due to inconsistency between site conditions and design properties, unexpected loads, and outdoor environment. The process of matching the measurement result with the analysis result can be confirmed in the safety management system. Conclusion: Gateway was used to communicate with real-time measurement results and safety management system program. It was made possible to preemptively respond to risk factors that may occur in the field.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.486-490
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• 2017

도심지에서의 침수피해는 이상홍수 및 국지성 호우 시 우수관거 시설기준 미달, 펌프장 등 배수시설이 설치되지 않아 하천의 계획홍수위보다 제내지의 지반고가 낮은 저지대 지역에서 많이 발생하고 있다. 특히, 내수침수의 경우는 외수에 따른 범람보다는 국민의 재산과 인명피해에 직접적인 영향을 미치므로 침수피해 위험도가 높은 지역의 주민에게 그 지역의 침수빈도와 범위를 인지시키고 사전대응 능력을 향상시킬 필요가 있다. 따라서 연구의 목적으로 매년 피해가 발생한 이력이 있는 위험지구에 대해 전국단위 시군구별 침수피해 지도를 작성하여 침수심 산정과 피해액 예측할 수 있는 기초자료로 활용하고, 주민들의 신속한 대처를 통해 그들의 생명과 재산을 보호하여 재난 안전 국가 이미지 제고에 기여하고자 한다. 본 연구에서는 도심지 유출모형인 XP-SWMM을 활용하여 내수재해 위험요인에 대한 전국을 해석하는 것에 한계가 있어 풍수해저감종합계획에 수록된 XP-SWMM모의 분석 결과 값을 활용하고자 하였다. 기 수립된 전국 풍수해저감종합계획의 과거 피해 자료를 바탕으로 이상 집중호우나 태풍의 내습 시 풍수해 피해 발생 가능성이 제일 높은 지역을 연구범위 대상지역으로 선정하였다. 그 중 풍수해의 주요 원인으로서 태풍, 집중호우 및 해일로 인한 피해발생 빈도가 높은 지역이면서 하천재해 및 내수침수 피해가 많은 경기도 동두천시를 연구대상 지역으로 선정하였으며, 대상지 유역 현황과 지형정보 및 빈도별 침수심을 조사하였다. 수록된 내용에 따르면 경기도 동두천시는 우수관망의 밀도가 높은 4개 위험지구를 내수재해 발생가능성 지역으로 선정하여 10년, 20년, 30년, 50년, 100년, 200년 6개 빈도에 대해 XP-SWMM 모의를 실시하였다. 이와 같이 수록된 각 빈도에 대한 모의 결과 값을 GIS기술을 이용하여 디지털화 하고 부가적인 분석을 위한 GIS데이터화 하는 내삽법을 선정하여 침수면적 및 침수심을 산출하였다. 그러나 면적비교를 통해 모의 결과 값을 디지털화 하는 과정에서 많은 오차가 발생되는 것을 확인하였고, 이를 보완하기 위해 좌표보정 자동화 프로그램을 개발하여 이러한 문제점을 제거하여 신뢰도를 향상시켰다. 이렇게 계산된 연구 대상지역의 침수심과 침수면적을 활용하여 지도제작 표준 지침서 및 가이드라인을 제시하여 한국형 호우피해 지도제작 기술개발에 기여하고, 비구조적 대책으로서 이상홍수에 대한 위험도를 파악하여 지역별 도심침수 방지를 위한 대비체계를 구축하는 등 위험지역에 대한 사전분석 및 활용에 기초자료로 도움이 되고자 한다.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.137-151
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• 2018

Recently, National interest has been rising due to earthquakes in Gyeongju and Pohang, disasters caused by landslides, landslides, and sinkholes around construction sites, and damage caused by disasters. SAR is able to detect ground displacement in mm for wide area, collect data through satellite, predict timeliness of crustal change by time series analysis, and reduce disaster and disaster damage. The purpose of this study is to investigate the latest SAR interference analysis technique (SqueeSAR analysis technique) of Sentinel-1A satellite (SAR sensor) of European ESA for about 3 years by selecting the 1st landfill site in the metropolitan area in Incheon, The settlement amount was calculated in a time series. Especially, in order to examine the accuracy of the subsidence and subsidence tendency by the SqueeSAR analysis method, the ground level survey was compared and analyzed for the first time in Korea. Also, the tendency of the subsidence trend was predicted by analyzing the time series and correlation trend of the subsidence for three years. Through this study, it is expected that disaster prevention and disaster prevention such as sinkhole and landslide can be utilized from time series monitoring of crustal variation of the land.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.474-484
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• 2019

In this age, where the social environment is changing rapidly and unpredictably, interest in safety from crime is increasing in Korean society. As the desire to live a life free from the fear of crime increases, interest in the construction of safe cities is also rising nationwide. To meet the national demand, the Korean government is promoting a project to link public disaster safety systems by involving municipalities, 112, 119, and other emergency services and institutions through the Smart City Integrated Platform in order to construct a smart safety net. This study investigates the linking of theSmart City Integrated Platform and theIntelligent Security Project. The results are as follows. 1. The linkage's objective is clear. 2. The system sector can provide information to accident-related organizations. 3. The scenario area can be expanded to a crime-prevention sector, and a long-term urban information integration infrastructure can be created. 4. Product testing is enabled by a smart city road map and through continuous consultation with relevant organizations. 5. Project diffusion to other local governments can be promoted with the continued addition of commercial products.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.374-385
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• 2022

Purpose: Even if a damage is applied to the dust of the construction site containing the first-class carcinogen, it is dismissed or 5~30% of the amount of noise damage compensation is paid., Because of such loopholes, some construction companies are neglecting the dust management of the construction site, and the damage of the workers and the residents in the construction site continues. Method: The purpose of this study is to examine the problems of the calculation criteria of damage compensation amount of construction site dust, the measurement of dust concentration, the analysis of measurement data (the data of electric signboard measuring device by the mining scattering method), the prediction and evaluation methods such as modeling, and to suggest improvement measures. Result: It is found that it is impossible to calculate the amount of damages from dust damage in the construction site by calculating the current dust damage compensation amount and dust concentration modeling and measurement. Conclusion: It will receive an application for compensation for damage within the site where damage is expected (about 100m in the straight line and the boundary line of the site), and present a method of calculating the amount of compensation that differentially evaluates dust damage to the degree of dust management and compliance with dust-related legal standards.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.453-462
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• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.216-230
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• 2023

Purpose: The purpose is to prevent accidents by predicting disasters through the analysis of near-miss. Method: In this study, a near-miss literature review and data were collected at construction sites, and a questionnaire survey was conducted to use logistic regression analysis and decision tree analysis to classify the possibility of near-miss connection. Result: As a result of analyzing the effects of near-miss types on mental, physical, and safety habits and behaviors, the factor with a high influence on the body is the need for near-miss management, the type of job is electricity·information communication, and health status in order, and the mental factor is the construction scale The influence was high, and the factors with the highest influence on the habit behavior factors were analyzed in the order of experience, number of serious injuries, and occupation in order of illusion, inappropriate work instructions, and body parts. Through decision tree analysis, factors and patterns that affect the possibility of a near-miss being a surprise accident were identified. Conclusion: Construction site officials consider the observation of near-miss and mentally and physically. Specific management of the relevance of physical aspects to near-miss should be implemented, and a work environment in which serious accidents are reduced is expected through personnel allocation, work plans, work procedures and methods, and feedback so that inappropriate work instructions do not lead to near-miss.

• 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 Society of Disaster Information
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• v.20 no.1
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• pp.156-168
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• 2024

Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.