• Proceedings of the Korean Geotechical Society Conference
• /
• 2009.09a
• /
• pp.233-242
• /
• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.1365-1371
• /
• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.2C
• /
• pp.59-69
• /
• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• Journal of Korean Society of Forest Science
• /
• v.90 no.3
• /
• pp.324-330
• /
• 2001

This study was carried out to develop prediction model for fill slope failure of forest road in igneous rock area using fuzzy theory which is non-linear model. The results were summarized as follows. The importance weight of factors on fill slope failure was ranked in the order of fill slope length, fill slope gradient, soil type, aspect, road position and longitudinal slope form. The degree of potential slope failure was high mainly under the such conditions as fill slope length greater than 8m, fill slope gradients steeper than $40^{\circ}$, constituent material with weathered rock, aspect of NE and road on ridge position. The optimal prediction model was developed with 0.15 of optimal coefficient(c) and 3.1165 of ${\lambda}$-value when fuzzy integral value of slope failure possibility is more than 0.5. And the discriminant accuracy was 86.8%, which shows the high availability for discrimination.

• Structural Engineering and Mechanics
• /
• v.67 no.2
• /
• pp.105-113
• /
• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• The Journal of Engineering Geology
• /
• v.18 no.1
• /
• pp.45-54
• /
• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model, which is one of the statistical analysis methods. The slope hazards data of Seoul and Kyonggi Province, which were induced by heavy rainfall in 1998, were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. Among these data, the number of data occurred slope hazards was 34 sections and the number of data non-occurred slope hazards was 27 sections. The statistical analyses using the decision tree model were applied to chi-square statistics, gini index and entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320 m, respectively.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2005.10a
• /
• pp.655-662
• /
• 2005

The objective of this thesis is to develop a prediction system of potential landslide sites to apply to the prevention of landslide disaster which occurred during the heavy rainfall in the rainy season. The system was developed by combining a modified slope stability analysis model and a hydrological model. The modified slope stability analysis model, which was improved from 1-D infinite slope stability analysis model, has been taken into consideration of the flexion of the hill slopes. To evaluate its applicability to the prediction of landslides, the data of actual landslides were plotted on the predicted areas on the GIS map. The matching rate of this model to the actual data was 92.4%. And the relations between wetness index and landform factors and potential landslide were analyzed.

• Economic and Environmental Geology
• /
• v.36 no.4
• /
• pp.313-320
• /
• 2003

Slope disaster is one of the repeated occurring geological disasters in rainy season resulting in about 23 human losses in Korea every year. The slope disaster, however, mainly depends on the spatial and climate properties. such as geology, geomorphology, and heavy rainfall, and, hence, the prediction or hazard analysis of the slope disaster is a difficult task. Therefore, GIS and various statistical methods are implemented for slope disaster analysis. In particular, GIS technique is widely used for the analysis because it effectively handles large amount of spatial data. The GIS technique. however, only considers the statistics between slope disaster occurrence and related factors, not the mechanism. Accordingly. an infinite slope model that mechanically considers the balance of forces applied to the slope is suggested here with GIS for slope disaster analysis. According to the research results, the infinite slope model has a possibility that can be utilized for landslide prediction and hazard evaluation since 87.5％ of landslide occurrence areas have been predicted by this technique.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.10 no.3
• /
• pp.165-175
• /
• 2006

To analyze the deformation and failure of slopes, generally, two types of model, Polynomial model and Growth model, are applied. These two models are focused on the behavior of the slope by time. Therefore, this research is more focused on predicting of slope failure than analyzing the slope behavior by time. Generally, Growth model is used to analyze the soil slope, to the contrary, Polynomial model is used for rock slope. However, 3-degree polynomial($y=ax^3+bx^2+cx+d$) is suggested to combine two models in this research. The main trait of this model is having an asymptote. The fields to adopt this model are Gosujae Danyang(soil slope) and Youngduk slope(rock slope), which are the cut-slope near national road. Data from Gosujae are shown the failure traits of soil slope, to the contrary, those of Youngduk slope are shown the traits of rock slope. From the real-time monitoring data of the slope, 3-degree polynomial is proved as excellent system to analyze the failure and behavior of slope. In case of Polynomial model, even if the order of polynomials is increased, the $R^2$ value and shape of the curve-fitted graph is almost the same.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.47 no.6
• /
• pp.37-45
• /
• 2005

Almost every year, Korea has been suffered from serious damages of lives and properties, due to landslides that are triggered by heavy rains in monsoon season. In this paper, we systematized the physically based landslide prediction model which consisted of 3 parts, infinite slope stability analysis model, groundwater flow model and soil depth model. To evaluate its applicability to the prediction of landslides, the data of actual landslides were plotted on the predicted areas on the GIS map. The matching rate of this model to the actual data was $84.8\%$. And the relation between hydrological and land form factors and potential landslide were analyzed.