• Tunnel and Underground Space
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• v.34 no.4
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• pp.330-354
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• 2024

In site characterization of high-level radioactive waste, discontinuity(joint) distribution and rock mass classification, which are key evaluation parameters in the rock engineering field, were evaluated using deep boreholes in the Wonju granite and Chuncheon granite, which belong to Mesozoic Jurassic era. To evaluate joint distribution characteristics, fracture zones and joint surfaces extracted from ATV data were used, and major joint sets were evaluated along with joint frequency according to depth, dip direction, and dip. Both the Wonju and Chuncheon granites that were studied showed a tendency for the frequency of joints to increase linearly with depth, and joints with high angles were relatively widely distributed. In addition, relatively large amounts of weathering tended to occur even in deep depth due to groundwater inflow through high-angle joints. RQD values remained consistently low even at considerable depth. Meanwhile, joint groups with low angles showed different joint characteristics from joint sets with high angles. Rock mass classification was performed based on RMR system, and along with rock mass classification for 50 m intervals where uniaxial compressive strength was performed, continuous rock mass classification according to depth was performed using velocity log data and geostatistical techniques. The Wonju granite exhibited a superior rock mass class compared to the Chuncheon granite. In the 50 m interval and continuous rock mass classification, the shallow part of the Wonju granite showed a higher class than the deep part, and the deep part of the Chuncheon granite showed a higher class than the shallow part.

• The Journal of Engineering Geology
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• v.34 no.3
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• pp.415-427
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• 2024

This study undertook a quantitative analysis of the distribution of fractures in deep drillcore from a Precambrian metamorphic complex on the north face of Hongcheon-gun, Gangwon-do, Korea. The fracture distribution with depth, inclination of fractures, and grain size in the fracture zone were measured and statistical techniques applied to derive probability distributions of fracture intervals. Analysis of the inclination angles of fracture planes showed that sub-horizontal fractures are dominant, and fracture spacing is mainly ≤0.5 m, with a median of 0.09 m, first quartile of 0.04 m, and third quartile of 0.18 m, indicating very dense fracture development. Statistical analysis of joint properties was undertaken with fitting using five probability density functions (double Weibull, exponential, generalized logistic, gamma, and lognormal). The lognormal distribution (sum of squared errors, SSE = 2.80) yielded the best fit based on the sum of residual squares. Quantitative characterization of the fracture characteristics of deep bedrock in the Hongcheon area is important for various geotechnical applications such as groundwater flow modeling, slope stability assessment, and underground structure design. In future studies, it will be necessary to combine in situ stress measurements and geophysical surveys to determine the relationship between fracture development and the local stress field.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.737-760
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• 2022

For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations. Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment. To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries. As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., ³⁴S and ¹⁸O of SO₄^2-, ¹³C and ¹⁴C of DIC, ²H and ¹⁸O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe²⁺/Fe³⁺, H₂S/SO₄^2-, NH₄⁺/NO₃^-). A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface. In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling). The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling. However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data. Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.374-392
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• 2024

In order to successfully select a site for deep geological disposal of high-level radioactive waste, it is important to perform the stepwise approach along with the systematic selection and survey of evaluation parameters of geological environmental characteristics suitable for the domestic geological environment. In this study, we evaluated the characteristics of hydraulic conductivity, which is considered the most important evaluation parameter in the field of hydrogeology, targeting a deep-depth rock aquifer where actual disposal facilities are expected to be located. In particular, for the first time in Korea, we obtained in-situ pressure-flow data by directly conducting hydraulic tests in boreholes at depths ranging from 500 m to 750 m in various rock types distributed in Korea (granite/volcanic rock/gneiss/mudstone). And we derived hydraulic conductivity values by rock types and depth using verified analytical methods. For this purpose, precision hydraulic testing equipment developed in-house through this study was used, and detailed investigation procedures based on standard test methods were applied to field tests. As a result of the analysis, the average hydraulic conductivity value was found to be in the range of 10^-9 m/s in all granite/volcanic rock/gneiss areas. In the mudstone area, an average hydraulic conductivity value of 10^-11 m/s was derived, which was about 100 times (2 orders of magnitude) lower than that of the fractured rock aquifers. Moreover, permeability tended to slightly decrease with depth in fractured rock aquifers (granite and volcanic rock areas) containing many rock fractures. The gneiss area tended to have large local differences in permeability according to the composition of the stratum and the development of fracture zones rather than depth. In mudstone areas with weak fracture development, there was no significant variation in rock permeability according to depth. The hydraulic conductivity results by various rock types and depth presented in this study are expected to be utilized in building a foundational database for the site selection, design, and construction of disposal facilities in Korea.

• Geomechanics and Engineering
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• v.16 no.3
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• pp.273-284
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• 2018

A detailed understanding of the mechanical behaviors for crushed coal rocks after grouting is a key for construction in the broken zones of mining engineering. In this research, experiments of grouting into the crushed coal rock using independently developed test equipment for solving the problem of sampling of crushed coal rocks have been carried out. The application of uniaxial compression was used to approximately simulate the ground stress in real engineering. In combination with the analysis of crack evolution and failure modes for the grouted specimens, the influences of different crushed degrees of coal rock (CDCR) and solidified grout strength (SGS) on the mechanical behavior of grouted specimens under uniaxial compression were investigated. The research demonstrated that first, the UCS of grouted specimens decreased with the decrease in the CDCR at constant SGS (except for the SGS of 12.3 MPa). However, the UCS of grouted specimens for constant CDCR increased when the SGS increased; optimum solidification strengths for grouts between 19.3 and 23.0 MPa were obtained. The elastic moduli of the grouted specimens with different CDCR generally increased with increasing SGS, and the peak axial strain showed a slightly nonlinear decrease with increasing SGS. The supporting effect of the skeleton structure produced by the solidified grouts was increasingly obvious with increasing CDCR and SGS. The possible evolution of internal cracks for the grouted specimens was classified into three stages: (1) cracks initiating along the interfaces between the coal blocks and solidified grouts; (2) cracks initiating and propagating in coal blocks; and (3) cracks continually propagating successively in the interfaces, the coal blocks, and the solidified grouts near the coal blocks. Finally, after the propagation and coalescence of internal cracks through the entire specimens, there were two main failure modes for the failed grouted specimens. These modes included the inclined shear failure occurring in the more crushed coal rock and the splitting failure occurring in the less crushed coal rock. Both modes were different from the single failure mode along the fissure for the fractured coal rock after grouting solidification. However, compared to the brittle failure of intact coal rock, grouting into the different crushed degree coal rocks resulted in ductile deformation after the peak strength for the grouted specimens was attained.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.312-338
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• 2023

The underground disposal facility for spent nuclear fuel demands a specialized design, distinct from conventional practices, to ensure long-term thermal, mechanical, and hydraulic integrity, preventing the release of radioactive isotopes from high-temperature spent nuclear fuel. SKB has established design criteria for such facilities and executed practical design implementations for Forsmark. Moreover, in response to subsurface uncertainty, SKB has proposed an empirical approach involving monitoring and adaptive design modifications, alongside stepwise development. SKB has further introduced a unique support system, categorizing ground types and behaviors and aligning them with corresponding support types to confirm safety through comparative analyses against existing systems. POSIVA has pursued a comparable approach, developing a support system for Onkalo while accounting for distinct geological characteristics compared to Forsmark. This demonstrates the potential for domestic implementation of spent nuclear fuel disposal facility designs and the establishment of a support system adapted to national attributes.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.365-384
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• 2024

The development of scanning technology is accelerating for safer and more efficient automated inspection than human-based inspection. Research on automatically detecting facility damage from images collected using computer vision technology is also increasing. The pixel size, quality, and quantity of an image can affect the performance of deep learning or image processing for automatic damage detection. This study is a basic to acquire high-quality raw image data and camera performance of a mobile tunnel scanning system for automatic detection of damage based on deep learning, and proposes a method to quantitatively evaluate image quality. A test chart was attached to a panel device capable of simulating a moving speed of 40 km/h, and an indoor test was performed using the international standard ISO 12233 method. Existing image quality evaluation methods were applied to evaluate the quality of images obtained in indoor experiments. It was determined that the shutter speed of the camera is closely related to the motion blur that occurs in the image. Modulation transfer function (MTF), one of the image quality evaluation method, can objectively evaluate image quality and was judged to be consistent with visual observation.