• Geomechanics and Engineering
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• 제21권6호
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• pp.551-564
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• 2020

High-stress and complex geological conditions impose great challenges to maintain excavation stability during deep underground mining. In this research, large anisotropic deformation and its management by support system at a deep underground mine in Western Australia were simulated through three-dimensional finite-difference model. The ubiquitous-joint model was used and calibrated in FLAC3D to reproduce the deformation and failure characteristics of the excavation based on the field monitoring results. After modeling verification, the roles of mining depth also the intercept angle between excavation axis and foliation orientation on the deformation and damage were studied. Based on the results, quantitative relationships between key factors and damage classifications were presented, which can be used as an engineering tool. Subsequently, the performance of support system installation sequences was simulated and compared at four different scenarios. The results show that, first surface support and then reinforcement installation can obtain a better controlling effect. Finally, the influence of bolt spacing and ring spacing were also discussed. The outcomes obtained in this research may play a meaningful reference for facing the challenges in thin-bedded or foliated ground conditions.

• 지구물리와물리탐사
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• 제12권1호
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• pp.105-113
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• 2009

관측된 파형의 특성들을 해석하여 굴절파와 광각으로 도달한 반사파를 분석하면 결과물로 얻어지는 지각구조 모델의 신뢰도를 향상시킬 수 있으며, 균일한 격자상의 그래프 이론에 기반한 파면법은 복잡한 구조에서도 주시와 파선경로를 효과적으로 계산할 수 있으나, 기본적으로 초동만을 이용하게 되는 단점이 있다. 이번 연구에서는 초동뿐만 아니라 후기 시간대에 도달하는 반사파와 굴절파 및 변환된 P파와 5파의 주시와 파선경로를 계산하기 위하여 다층 모델상에서 표현되는 slowness network 노드에 기반한 수정 파면법을 이용하는 새로운 알고리즘을 개발하였다. 새로운 알고리즘을 통해 모호면의 형태와 변환된 P파와 S파의 위상을 획득하기 위하여 후기 시간대의 Pg파, Pn파 이후에 들어오는 강한신호의 파, 모호면에서 중첩된 PmP를 분석하였다. 제안된 알고리즘의 효용성을 검증하기 위하여 해양-대륙의 전이대와 해령 및 해산에 러한 모델링 연구를 수행하였으며, 2차원 유한차분법을 이용한 수치모형을 통해 그 효용성을 확인하였다. 초동 주시만을 해석에 사용할 경우 대륙-해양 전이대와 해령 및 해산과 같은 모델에서 획득되는 도달파들과 파선경로들의 특성이 각기 다르게 나타나 많은 해석상의 오류가 발생할 수 있는 위험성이 있기 때문에 제안된 기법을 통해 효과적인 해석을 수행할 수 있을 것이다.

• 지질공학
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• 제12권3호
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• pp.345-360
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• 2002

이 연구는 활동 가능성이 높은 퇴적암 사면에 대한 안정성을 분석하기 위해 수치해석을 수행하고, 안정성을 확보할 수 있는 보강공법을 제시하기 위한 목적으로 수행되었다. 연구지역은 백악기 하양층군의 학봉 현무암층에 해당하는 지역으로 대부분이 현무암질 응회암으로 구성되어 있으며, 지질 구조요소로는 크게 1차 구조 요소인 층리와 2차 구조요소인 절리 및 단층이 확인되었다 층리는 단사구조를 보이는데, 대부분의 경우 경사방향이 120~160/15~25(dip direction/dip)이고, 절리의 경우에는 크게 3개 조의 절리군이 확인되는데 세트 1은 310~330/65~85, 세트 2는 230~250/70~85이며, 세트 3은 뚜렷한 연장성을 보이지는 않지만 020의 경사방향에 85$^{\circ}$이상의 고각을 보이고 있다. 사면에 대한 안정성 해석은 운동학적 해석과 한계평형 해석을 수행한 후, 수치해석 방법으로 FLAC을 이용하였다. FLAC은 유한차분법을 이용하는 연속체 역학모델이지만, Interfaces Module을 이용하여 UDEC과 같은 불연속체 모델의 해석효과를 얻을 수 있었다.

• 한국터널지하공간학회 논문집
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• 제25권6호
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• pp.403-421
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• 2023

도심지 내 지하구조물 개발의 필요성이 증가함에 따라, TBM 터널 시공 중 터널 굴진면 전방예측에 대한 연구가 꾸준하게 진행되고 있다. 본 연구에서는 TBM 터널 굴착 중 복합지반을 조우하는 상황을 모사한 유한요소(finite element) 수치해석 모델을 개발하였다. 개발된 수치해석 모델은 이론해와 실내실험으로부터 측정된 전기 비저항 결과값과의 비교를 통해 그 성능을 검증하였다. 이후 실제 터널의 형상과 지반조건, 측정전극의 배열 조건 등 전기 비저항 탐사에 대한 영향 변수를 설정하고 이에 따른 매개변수 해석을 수행하였다. 그 결과, 복합지반 내 경계면의 경사가 가파를수록, 복합지반을 구성하는 두 지반 사이의 전기 비저항 차이가 클수록, TBM 굴착 중 전기 비저항 측정값이 더 급격하게 변화함을 확인하였다. 또한, 보다 효율적이고 정확한 복합지반 예측을 위해 적절한 전극 간격 및 전극 배열 위치 선정의 중요성을 제고하였다. 결론적으로, 본 연구에서 개발된 수치해석 모델을 통한 터널 막장면 전방 복합지반 예측은 TBM 터널 시공 과제의 구조적 안정성과 경제적 효율성 증대에 이바지할 것으로 사료된다.

• 터널과지하공간
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• 제30권3호
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• pp.193-213
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• 2020

사용후핵연료 심층처분장 처분공에 설치된 압축 벤토나이트 완충재가 처분공 내벽에 형성된 굴착손상영역 불연속면 내로 침투하는 현상을 보다 더 현실적으로 모사할 수 있는 수학적 모델이 개발되었다. 이 모델에서는 압축 벤토나이트의 침투를 평행 평판 암반 절리을 통한 Bingham 유체의 이동으로 가정한다. 개발된 모델에 의해 벤토나이트의 침투현상을 분석한 결과, 암반 절리를 통해 압축 벤토나이트가 침투하는 최대 깊이는 포화 압축 벤토나이트의 팽윤압과 암반 절리의 폭에 비례하며, 압축 벤토나이트의 항복강도에 반비례하였다. 압축 벤토나이트의 점도는 압축 벤토나이트의 침투 속도를 좌우하나, 최대 침투깊이에는 영향을 미치지 않는다.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 방사선산업학회지
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• 제17권1호
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• pp.33-44
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• 2023

The deep geological repository of high-level radioactive waste shall be designed to meet the safety objective set in the form of radiation dose or corresponding risk to protect human and the environment from radiation exposure. Engineering feasibility and conformity with the safety objective of the facility conceptual design can be demonstrated by comparing the assessment result using the computational model for scenario(s) describing the radionuclide release and transport from repository to biosphere system. In this study, as the preliminary study for developing the high-level radioactive waste disposal facility in Korea, we reviewed and analyzed the entire list of FEPs and how to handle each FEP from a general point of view, which are selected for the geosphere region in the radiological safety assessment performed for the license application of the KBS-3 type deep geological repository in Finland and Sweden. In Finland, five FEPs (i.e., stress redistribution, creep, stress redistribution, erosion and sedimentation in fractures, methane hydrate formation, and salt exclusion) were excluded or ignored in the radionuclide release and transport assessment. And, in Sweden, six FEPs (i.e., creep, surface weathering and erosion, erosion/sedimentation in fractures, methane hydrate formation, radiation effects (rock and grout), and earth current) were not considered for all time frames and earthquake out of a total of 25 FEPs for the geosphere. Based on these results, an FEP list (draft) for the geosphere was derived, and the relative importance of each item was evaluated for conducting the radiological safety assessment of the domestic deep geological disposal facility. Since most of information on the disposal facility in Korea has not been determined as of now, it is judged that all FEP items presented in Table 3 should be considered for the radiological safety assessment, and the relative importance derived from this study can be used in determining whether to apply each item in the future.

• Geomechanics and Engineering
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• 제37권1호
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• Nuclear Engineering and Technology
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• 제42권1호
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• pp.9-16
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• 2010

Experimental examinations for evaluating fracutres were conducted by using transparent replicas of a single fracture in order to obtain the fracture data to contribute to the methodlogy on how to improve the definitaion of representative parameter values used for a parallel plate fracture model. Quantitative aperture distribution and quantitative tracer concentration data at each point in time were obtained by measuring the attenuation of transmitted light through the fracture in high spatial resolution. the representative aperture values evaluated from the multiple different measurement methods, such as arithmetic mean of aperture distribution measured by the optical method, transport aperture evaluated from the tracer test, and average aperture evaluated from the fracture void volume measurement converged to a unique value that indicates the accuracy of this experimental study. The aperture data was employed for verifying the numerical simulation under the assuption of Local Cubic Law and showed that the calculated flow rate through the fracture is 10%-100% larger than hydraulic test results. The quantitative tracer concentration data is also very valuable for validating existing numerical code for advection dispersion transport in-plane heterogeneous fractures.

• Geomechanics and Engineering
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• 제23권5호
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• pp.441-454
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• 2020

The tunnel collapse, large deformation of surrounding rock, water and mud inrush are the major geological disasters in soft rock tunnel construction. Among them, tunnel collapse has the most serious impact on tunnel construction. Current research backed theories have certain limitations in identifying the collapse risk of soft rock tunnels. Examining the Zhengwan high-speed railway tunnel, eight soft rock tunnel collapse influencing factors were selected, and the combination of indicator weights based on the analytic hierarchy process and entropy weighting methods was obtained. The results show that the groundwater condition and the integrity of the rock mass are the main influencing factors leading to a soft rock tunnel collapse. A comprehensive fuzzy evaluation model for the collapse risk of soft rock tunnels is being proposed, and the real-time collapse risk assessment of the Zhengwan tunnel is being carried out. The results obtained via the fuzzy evaluation model agree well with the actual situation. A tunnel section evaluated to have an extremely high collapse risk and experienced a local collapse during excavation, verifying the feasibility of the collapse risk evaluation model. The collapse risk evaluation model proposed in this paper has been demonstrated to be a promising and innovative method for the evaluation of the collapse risk of soft rock tunnels, leading to safer construction.