• Journal of the Korean Geotechnical Society
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• v.24 no.10
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• pp.57-70
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• 2008

The advantage of piezocone penetration test is a guarantee of continuous data, which is a source of reliable interpretation of target soil layer. Many researches have been carried out f3r several decades and several classification charts have been developed to classify in-situ soil from the cone penetration test result. Since most present classification charts or methods were developed based on the data which were compiled over the world except Korea, they should be verified to be feasible for Korean soil. Furthermore, sometimes their charts provide different soil classification results according to the different input parameters. However, unfortunately, revision of those charts is quite difficult or almost impossible. In this research a new soil classification model is proposed by using fuzzy C-mean clustering and neuro-fuzzy theory based on the 5371 CPT results and soil logging results compiled from 17 local sites around Korea. Proposed neuro-fuzzy soil classification model was verified by comparing the classification results f3r new data, which were not used during learning process of neuro-fuzzy model, with real soil log. Efficiency of proposed neuro-fuzzy model was compared with other soft computing classification models and Robertson method for new data.

• Tunnel and Underground Space
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• v.11 no.4
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• pp.311-318
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• 2001

RQD is used to evaluate the degree of fracture in the rock mass and is also used as input into rock mass classification scheme, such as RMR and Q-system. However there are some drawbacks of the RQD caused by anisotropy and calculation length. Thus it is important to understand the variation of RQD in 3-D space in order to evaluate the properties of rock mass. The main purpose of this study is to reveal the distribution of RQD in the equal-angle stereo net, to investigate the effects of scanline length and joint frequency and to inquire the effect on the selection of rock mass strength parameters in the numerical analysis. Analysis has been extended to field joint survey data using same method. The results can be applied to contribute for more accurate interpretation of the results of engineering geological survey for better design and construction work.

• Journal of Soil and Groundwater Environment
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• v.22 no.3
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• pp.18-26
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• 2017

The increasing frequency of droughts has been increasing the necessity of utilizing subsurface dams as reliable groundwater resources in areas where it is difficult to supply adequate agricultural water using only surface water. In this study, we analyzed the current status and actual conditions of five agricultural subsurface dams as well as the effect of obtaining additional groundwater from subsurface dams operated as one aspect of the sustainable integrated water management system. Based on the construction methods and functions of each subsurface dam, the five subsurface dams are classified into three types such as those that derive water from rivers, those that prevent seawater intrusion, and those that link to a main irrigation canal. The classification is based on various conditions including topography, reservoir location, irrigation facilities, and river and alluvial deposit distributions. Agricultural groundwater upstream of subsurface dams is obtained from four to five radial collector wells. From the study, the total amount of groundwater recovered from the subsurface dam is turned out to be about 29~44% of the total irrigation water demand, which is higher than that of general agricultural groundwater of about 4.6%.

• 한국지구물리탐사학회:학술대회논문집
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• 2009.05a
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• pp.35-45
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• 2009

Reservoir Characterization (RC) using 3-D seismic attributes analysis can provide properties of the oil sand reservoirs, beyond seismic resolution. For example, distributions and temporal bed thicknesses of reservoirs could be characterized by Spectral Decomposition (SD) and additional seismic attributes such as wavelet classification. To extract physical properties of the reservoirs, we applied 3-D seismic attributes analysis to the oil sand reservoirs in McMurray formation, in BlackGold Oilsands Lease, Alberta Canada. Because of high viscosity of the bitumen, Enhanced Oil Recovery (EOR) technology will be necessarily applied to produce the bitumen in a steam chamber generated by Steam Assisted Gravity Drainage (SAGD). To optimize the application of SAGD, it is critical to identify the distributions and thicknesses of the channel sand reservoirs and shale barriers in the promising areas. By 3-D seismic attributes analysis, we could understand the expected paleo-channel and characteristics of the reservoirs. However, further seismic analysis (e.g., elastic impedance inversion and AVO inversion) as well as geological interpretations are still required to improve the resolution and quality of RC.

• Tunnel and Underground Space
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• v.10 no.3
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• pp.395-402
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• 2000

The influence of the mined-out caves on the stability of the high speed railway tunnel was investigated with a series of geological logging and in-situ tests on the one hand, and with the rock mass classification using the multiple regression analysis on the other hand. The rock mass in this area can be classified as 'fair', and the condition of the discontinuities plays the most important role in the classification of the rock mass. The results of the analysis obtained by the FLAC showed that the western part of the tunnel locating at 50m above the mine cavities could be affected by subsidence associated with a considerable deformation, the magnitude of which might depend on the properties of the rock mass.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.21-34
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• 2016

In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

• The Journal of Engineering Geology
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• v.15 no.4 s.42
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• pp.463-473
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• 2005

Because of using the same rating ranges for every rock types the RMR or the Q-system could not usually consider local geological characteristics They also could not present sufficiently the engineering anisotropy of rocks. The canonical correlation analysis was carried out with 3 kinds of face mapping data obtained from granite, sedimentary rock and phyllite in order to clarify a discrepancy between rock types. According to analysis results, as a type of rocks changes, RM factors have different influences on the total rating of RMR.

• The Journal of Engineering Geology
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• v.27 no.2
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• pp.153-164
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• 2017

The geological factors for causing ground subsidence are very diverse. It can be affected by any geological or extrinsic influences, and even within the same geological factor, the soil depression impact factor can be determined by different physical properties. As a result of reviewing a large number of papers and case histories, it can be seen that there are seven categories of ground subsidence factors. The depth and thickness of the overburden can affect the subsidence depending on the existence of the cavity, whereas the depth and orientation of the boundary between soil and rock are dominant factors in the ground composed of soil and rock. In case of soil layers, more various influencing factors exist such as type of soil, shear strength, relative density and degree of compaction, dry unit weight, water content, and liquid limit. The type of rock, distance from the main fracture and RQD can be influential factors in the bedrock. When approaching from the hydrogeological point of view, the rainfall intensity, the distance and the depth from the main channel, the coefficient of permeability and fluctuation of ground water level can influence to ground subsidence. It is also possible that the ground subsidence can be affected by external factors such as the depth of excavation and distance from the earth retaining wall, groundwater treatment methods at excavation work, and existence of artificial facilities such as sewer pipes. It is estimated that to evaluate the ground subsidence factor during the construction of underground structures in urban areas will be essential. It is expected that ground subsidence factors examined in this study will contribute for the reliable evaluation of the ground subsidence risk.

• Korean Journal of Ecology and Environment
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• v.46 no.1
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• pp.1-9
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• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Nuclear Engineering and Technology
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• v.43 no.6
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• pp.583-592
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• 2011

The radiological characteristics for waste classification were assessed for neutron-activated decommissioning wastes from a CANDU reactor. The MCNP/ORIGEN2 code system was used for the source term analysis. The neutron flux and activation cross-section library for each structural component generated by MCNP simulation were used in the radionuclide buildup calculation in ORIGEN2. The specific activities of the relevant radionuclides in the activated metal waste were compared with the specified limits of the specific activities listed in the Korean standard and 10 CFR 61. The time-average full-core model of Wolsong Unit 1 was used as the neutron source for activation of in-core and ex-core structural components. The approximated levels of the neutron flux and cross-section, irradiated fuel composition, and a geometry simplification revealing good reliability in a previous study were used in the source term calculation as well. The results revealed the radioactivity, decay heat, hazard index, mass, and solid volume for the activated decommissioning waste to be $1.04{\times}10^{16}$ Bq, $2.09{\times}10^3$ W, $5.31{\times}10^{14}\;m^3$-water, $4.69{\times}10^5$ kg, and $7.38{\times}10^1\;m^3$, respectively. According to both Korean and US standards, the activated waste of the pressure tubes, calandria tubes, reactivity devices, and reactivity device supporters was greater than Class C, which should be disposed of in a deep geological disposal repository, whereas the side structural components were classified as low- and intermediate-level waste, which can be disposed of in a land disposal repository. Finally, this study confirmed that, regardless of the cooling time of the waste, 15% of the decommissioning waste cannot be disposed of in a land disposal repository. It is expected that the source terms and waste classification evaluated through this study can be widely used to establish a decommissioning/disposal strategy and fuel cycle analysis for CANDU reactors.