• 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.

• Journal of the Korean GEO-environmental Society
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• v.11 no.12
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• pp.19-25
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• 2010

Inventory survey is process collecting data about cut slope. It's different from inspection. Collected data are being used get at status about cut slope along national roads. And we analyse between current situation and data about collapse slope. It helps finding better method about maintenance of cut slope. We found changes in the situation between data at present and data at 10 years ago. We will research collapse and counterplan by cause.

• The Journal of Engineering Geology
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• v.27 no.4
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• pp.417-427
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• 2017

This study aims to present cases of rock slope failures caused by geological structures. Status of slope failures, results of cause analysis and stabilizing methods are introduced, focusing primarily on rock slope failures caused by specific geologic structures, such as intersection of faults infilled with clay, foliation and fault shear zone by dike intrusion and deep-seated clayey layer along lithologic boundary. Detailed geological survey, geophysical exploration and boring survey were conducted for cause analysis. Stabilizing method to prevent further slope failures and to ensure long-term stability of slopes were established, considering characteristics of geological structures, types of failure and geological conditions.

• The Journal of Engineering Geology
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• v.21 no.4
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• pp.313-322
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• 2011

On September 2007, numerous slopes at Jeonranam-Do collapsed as a result of rainfall related to Typhoon Nari. Failure occurred at a road cut-slope on the ${\bigcirc}{\bigcirc}$ detour road, damaging transport infrastructure. This study aims to determine the cause of failure based on field investigations, the geotechnical properties of soil, clay mineral composition, and quantitative analysis. The studied cut slope consists of weathered soil that originated from volcanic rocks, and minor faults and a mafic dyke. Surface water tends to seep into the soil because the roadway is not sealed and because of poorly installed drainage. Sieve and XRD analyses indicate that soils in the failure zone are ML and CH, which are prone to swelling due to the presence of clay minerals such as smectite and vermiculite. The slope failed due to the improper construction of drainage facilities, the presence of geological weak zones, and high soil contents of swelling clay.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.508-522
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• 2019

Because of the slope failure, not only property damage but also human damage can occur, slope stability analysis should be conducted to predict and reinforce of the slope. This paper, defines the ground areas that can be characterized in terms of slope failure such as Rockmass jointset, Rockmass fault, Soil, Leakage water and Crush zone in sloped images. As a result, it was shown that the deep learning instance segmentation network can be used to recognize and automatically segment the precise shape of the ground region with different characteristics shown in the image. It showed the possibility of supporting the slope mapping work and automatically calculating the ground characteristics information of slopes necessary for decision making such as slope reinforcement.

• Journal of the Korean GEO-environmental Society
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• v.17 no.3
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• pp.27-35
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• 2016

Even if the various data analyzing methods were suggested to examine the measured slope behaviors, it is difficult to find methods or procedures for connecting the analyzed results of slope stability and measured slope data. This research suggests the analyzing methods combing the stability analysis and measured data based on progressive failure of slope. Slope failure analysis by time degradation were calculated by strength parameters composed of strength reduction coefficients, also which were compared to the measured data according to the variations of safety factor and displacement of slopes. The accumulated displacement curve were shown as 3rd degree polynomials by suggested procedures, which was the same as before researches. The reverse displacement velocity curves were shown as linear function for prediction of brittle slope failures, also they were shown as 3rd degree polynomials for ductile slope failures, which were the same as the suggested equation by Fukuzono (1985) and they were very similar behaviors to the in-situ failure cases.

• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.39-47
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• 2022

This study aims at evaluating the stability of disaster risks, such as schools, apartments, and geotechnical structures located around slopes in urban areas. The research conducted an aerial photography analysis on where the slope of the retaining wall behind 𐩒𐩒 High School in Gwangju collapsed in August 2018 due to heavy rain. In general, the overflow of rainwater has been managed through drainage channels around slopes during the rainy season, and the surface flow of rainfall was limited due to the presence of dense forests in the area. However, when the slope collapsed, a lot of water flowed out of the ground, and the saturated surface layer ground was destroyed. To analyze the cause, the changed terrain of the upper slope area, which could not be directly identified, was photographed using unmanned aerial vehicles. Digital Elevation Model by unmanned aerial vehicle shooting was performed by analyzing the slope map, calculating the direction of rainfall and the length and width of water-logged areas. The change in the instability of the slope over time due to a 10-day rainfall was also analyzed through numerical analysis.

• Journal of the Korean GEO-environmental Society
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• v.12 no.8
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• pp.25-31
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• 2011

In recent rainy seasons, severe rain storms have caused frequent reinforced retaining wall collapses and slope sliding which have lead to casualties. In this paper, investigating cases of reinforced retaining wall failure, the causes of cracks in reinforced retaining wall and slope sliding have been examined, and a finite element analysis considering the construction phase has been done to analyze the cause and characteristics of slope sliding. As a result, reinforced retaining wall displacement has increased due to heavy rain storms and the increase size has been shown to be large. From these results, it has been analyzed that pile driving can have an effect on the collapse of reinforced retaining walls.

• Journal of the Korean Geotechnical Society
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• v.27 no.1
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• pp.25-34
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• 2011

In general, many instrumentations of slope rely on theory or experience because on-site accessibility and long term instrumentation are difficult to conduct the instrumentation of slopes. Also the prediction of disaster is very difficult. Therefore experimental research was conducted about an effective method to predict collapse of slope and on-site applicability in this study. The collapse of slope was able to be predicted by applying AE sensor which we call WEAD to the failure criteria. The parameters of AE generated during the collapse of slope were secured through bending shear test. Test construction was applied to the slope with a history and a possibility of collapse. As a result, it is shown that AE parameters do not exceed the failure criterion and is found to be stable slopes. As the real symptoms of collapse did not appear, AE was found to have excellent applicability.