• The Journal of Engineering Geology
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• v.30 no.4
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• pp.649-661
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• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• The Journal of Engineering Geology
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• v.22 no.4
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• pp.417-425
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• 2012

CCTV cameras are used for surveillance and purposes of security, and can also be applied for monitoring infrastructure and equipment. In the Cut Slope Management System managed by KICT (Korea Institute of Construction Technology), cut slopes are continuously monitored using a real-time system, with CCTV cameras installed at 119 sites to detect slope activity. Here we compare CCTV images with displacement at three sites and perform a quantitative analysis. Methods for improving CCTV camera management and systems are also discussed with regard to communication, obstacles, and nighttime management.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.501-502
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• 2018

본 논문은 건설CALS시스템에 20년간 축적된 데이터를 활용하기 위해서 2018년부터 2020년까지 개발 예정인 과적단속 최적 위치 시기 예측 서비스, 비탈면 붕괴 위험도예측서비스, 도로점용(연결) 허가 가능구간 예측 서비스, 보상비 예측 서비스 등 4종의 건설CALS 빅데이터 서비스 기술 개발을 위한 현황분석을 기술하였다. 개발된 서비스는 2020년에 국토교통부 소속기관인 지방국토관리청과 국토관리사무소의 업무담당자 및 국민에게 시범 적용하여 빅데이터 서비스의 효과를 검증하고, 단계적으로 국토교통부 도로관리 업무에 적용할 계획이다.

• Korean Journal of Construction Engineering and Management
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• v.22 no.4
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• pp.29-40
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• 2021

Recently, interest in Building Information Modeling (BIM) has increased globally, and 3D modeling is a start for the application of BIM at construction sites. However, while many studies have been conducted on the efficiency of 3D modeling focused on civil facilities, there is a lack of research on the earthwork BIM. In particular, since 3D slope often has complex shapes depending on the ground models, the efficiency method for 3D slope are needed. This study analyzed the interfaces and procedures of other software to find out what functions users need. Then the functions to enter intervals between 3D faces, select multiple ground models, and improve the interface are reflected on the developed system and is able to efficiently perform modeling with only five steps, and reduce the number of clicks and inputs. As a result of conducting the test to verify the efficiency, using the developed system made skilled users complete modeling at least 1.8 times faster and unskilled people at least 2.4 times faster than using other software. This is expected to perform 3D slope modeling more efficiently, as well as to contribute to the activation of future BIM adoption for housing construction projects.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.5-14
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• 2010

The slope stability method using the soil stabilizer is a way to ensure that the slope stability from reinforcing method is environmentally friendly. However, the reinforcing method does not ensure slope stability for lack of research on the reinforcement effect of the mixture with soil. So the application of this method implies difficult technical issues. In this research, reinforcement effect is investigated according to the different ratio of mixture. And the optimum reinforcement depth is proposed according to the height of slope from numerical analysis. The results show that approximately the soil strength increases from two to three times. From numerical analysis, it is possible to estimate the optimum height according to the height of slope. It is anticipated that the use of soil stabilizer will increase the slope stability.

• Journal of the Korean GEO-environmental Society
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• v.12 no.8
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• pp.5-12
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• 2011

This study was performed to analyze the degree of rainfalls, the duration time of rainfalls, the inclination of slopes and other damage causes depending on the ground condition, when the landslide occurs by the rainfall. Based on that, a big and a small model soil box were compared to examine the features of landslides, which is ultimately for forecasting landslides. An artificial rainfall device was installed in the model box, and the large model box has a real-size slope in it. Also, various measures were fulfilled such as collapse forms, a pore pressure, an earth pressure and moisture contents in order to analyze the effect of the model box size on the landslide mechanism and to test the usability of a small model box as a material for landslide experiments.