• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.1532-1548
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• 2008

The tunnel type spillways is under construction to increasing water reservoir capacity in Dae-am dam. The tunnel outlet was planned to be made after installing slope stabilization system on natural slope there. Generally, the tunnel outlet is made perpendicularly to the slope, but in this case, it had to be made obliquely to the slope for not interrupting flow of river. Because of excavation in condition of natural slope caused to deflecting earth pressure, the outlet couldn't be made. So, artificial ground made with concrete that it was constructed in the outside of tunnel for producing the arching effect which enables to make a outlet. We were planned tunnel excavation was carried out after artificial ground made. Artificial ground made by poor mix concrete of which it was planned that the thickness was at least 3.0m height from outside of tunnel lining and 30cm of height per pouring. Spreading and compaction was planned utilized weight of 15 ton roller machine. In order to access of working truck, slope of artificial ground was designed 1:1.0 and applied 2% slope in upper pert of it for easily drainage of water. In addition to, upper pert of artificial ground was covered with soil, because of impaction of rock fall from upper slope was made minimum. The tunnel excavation of the artificial ground was designed application with special blasting method that it was Super Wedge and control blasting utilized with pre-percussion hole.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1342-1348
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• 2010

Currently available evaluation checklists are developed for specific purposed using different parameters and items determined by different weighting factors. Those items with different weighting are sometimes said that they are based on the engineering judgement and leap of faith and, therefore, there is a limitation to adapt those checklists for slope-stability evaluation in the field. This study reviews factors affecting slope stability, analyze the relationship between those factors and slope failures using artificial neural network, and proposed a slope-stability evaluation model for adequate weighting for the factors.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.759-763
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• 2010

Currently available evaluation checklists are developed for specific purposed using different parameters and items determined by different weighting factors. Those items with different weighting are sometimes said that they are based on the engineering judgement and leap of faith and, therefore, there is a limitation to adapt those checklists for slope-stability evaluation in the field. This study reviews factors affecting Rock-slope stability, analyze the relationship between those factors and slope failures using artificial neural network, and proposed a slope-stability evaluation model for adequate weighting for the factors.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1294-1301
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• 2010

This study is an experimental research for the dispersion behavior and impact characteristics of debris flow according to change of slope. Large scale experimental setup for the debris flow was established to simulate the artificial rainfall and control the ground slope. Parameters such as materials of debris flow, slope, and length of slope were used for the experiments. After the experiments, it was found that the speed of ground material components was increased about 28~47%. It was found that speed can be increased by increasing the particle size. Furthermore, maximum/final loads for ground material components were increased 89% for the coarse aggregate and 68% for the fine aggregate comparing with sand.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.8 no.1
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• pp.10-16
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• 2005

This experiment was conducted to study on application of several Korean native plants by seed spray methods for cut slope revegetation, and possibility of replacement almost imported tall fescue seeds by native herbaceous plants. So, we investigated growth and covering rate after sowing native plants seeds at the artificial slope plots in Suwon and the rock exposed cut-slopes in Wonju city. Emergence rate after seed spray at artificial slopes were higher Elsholtzia splendens and Dianthus superbus var. longicalycinus, showing the highest in E. splendens. Also, E. splendens, D. superbus var. longicalycinus, and Agrostemma coronaria were possible to use for seed spray at the rock exposed cut-slopes. The plots of mixed native plants show more seasonal scenery than that of tall fescue. Soil surface run-off by Typhoon was less in plot sown native plants than those of lawn grass, resulting fresh weight of roots was heavier. Thus, we found that the mixed seed spray of several native herbaceous plants, E. splendens, D. superbus var. longicalycinus, and Agrostemma coronaria, were well covered the slopes as tall fescue.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• The Korean Journal of Ecology
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• v.21 no.5_3
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• pp.713-721
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• 1998

The effects of artificial interference on the vegetation landscape in Mt. Nam of Seoul, Korea were clarified by analysing the distribution of vegetation landscape element and the number and size of patch depicted as a vegetation map in terms of landscape ecological principles. The effects of artificial interference on vegetation were also confirmed from the environmental gradient analysis on plant community extended from the lowland to the peak of that mountain. Vegetation landscape elements were divided into plantation and secondary forest in actual vegtation map. The ratio of plantation to secondary forest was higher in the lowland below mid-slope and the southern slope. Most afforested land were occupied by Robinia pseudoacacia and Populus tomentoglandulosa, Pinus rigida, P. koraiensis, Metasequoia glyptostroboides, Alnus hirsuta and so on are localy planted. In addition, projects to replace those afforested trees by P. densiflora as a kind of campaign for "Restoration of the one original feature of Mt. Nam" or to replace those tree species by planting young Abies holophylla or P. koraiensis under the mature afforested trees are also carried out in recent years. In cases of secondary forest, the southern slope was dominated by P. densiflora and the northern one by Q. mongolica. But the lowland of the northern slope is dominated by P. densiflora as the same as that in the southern slope. Vegetation landscape elements in Mt. Nam were much simplified comparing with that of suburban area around Seoul. The number of patches, which reflects the degree of diverse artificial interference was more in the lower area than in the upper area and more in the southern slope than in the northern one. On the other hand, the size of patch showed the antagonistic tendency to that of the number of patch. As a result of environmental gradient analysis, vegetation distribution in Mt. Nam was different from that in suburban area around Seoul. For example, Alnus japonica community, Zelkova serrata community, and Carpinus laxiflora community, which is established in mountain comparatively rare in artificial interference disappeared in Mt. Nam. As a result of analysis on vegetational succession in P. densiflora community and Q. mongolica community, both communities showed a tendency of retrogressive succession differently from that in control site located in suburban area around Seoul. In addition, species composition of P. densiflora and Q. mongolica communities in Mt. Nam were also different from those in Mt. Surak located around Seoul. It was interpreted that those results were originated from the environmental pollution and excessive arti ficial interferences.rferences.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.123-133
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• 2003

A practical statistical approach that can be used to model various sources of uncertainty systematically is presented in the context of reliability analysis of slope stability. New expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. The stochastic nature of seismic loading is studied by generating a large series of hazard-compatible artificial motions, and by using them in subsequent response analyses. The analyses indicate that in a seismically less active region such as the Korean Peninsular, a moderate variability in soil properties has an effect as large as the characterization of earthquake hazard on the computed risk of slope failure and excessive slope deformations.

• Smart Structures and Systems
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• v.29 no.4
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.