• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.3
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• pp.281-290
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• 2018

In order to understand the long-term behavior of radionuclides in granite environments, geochemical behavior characteristics of uranium in granitic host rock of KURT (KAERI Underground Research Tunnel) were investigated by dissolution experiment with different reaction time and solutions. In the dissolution experiment, significantly increased dissolution levels of uranium from granite powder samples were identified during the reaction time of 0~10 days for reaction solutions ($UD-CO_3$ and UD-Bg) containing a large amount of $CO_3{^{2-}}$. On the other hand, significantly increased dissolution levels of uranium were also identified for reaction solutions containing Na and Ca after 60 days. Dissolution of uranium continuously increased in reaction solutions of $UD-CO_3$ ($44.61{\mu}g{\cdot}L^{-1}$), UD-Bg ($41.01{\mu}g{\cdot}L^{-1}$), UD-Na ($26.87{\mu}g{\cdot}L^{-1}$), UD-Ca ($20.26{\mu}g{\cdot}L^{-1}$), UD-CaSi ($17.03{\mu}g{\cdot}L^{-1}$), and UD-Si ($10.47{\mu}g{\cdot}L^{-1}$) in the experimental period of ~270 days. However, after day 270, dissolution of uranium showed a decreasing tendency. This is thought to have occurred because existing uranium in granite samples reached the limit of dissolution by interaction with reaction solutions. Concentrations of dissolved uranium and points of maximum concentration value were found to differ depending on the $CO_3{^{2-}}$ presence in the mixed reaction solution and on the geochemical type of the water. It is estimated that differences in the reaction rate between the granite sample and the reaction solution are due to the influence of dissolved ions in the reaction solution.

• 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.32 no.5
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• pp.285-297
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• 2022

We report results of Extended Leak-Off Test (XLOT) conducted in a large diameter borehole, which is drilled for installation of deep borehole geophysical monitoring system to monitor micro-earthquakes and fault behavior of major fault zones in the southeastern Korean Peninsula. The borehole was planned to secure a final diameter of 200 mm (or more) at a depth of ~1 km, with 12" diameter wellbore to intermediate depths, and 7-7/8" (~200 mm) to the bottom hole depth. We drilled first the 12" borehole to approximately 504 m deep and installed American Petroleum Institute standard 8-5/8" casing, then annulus between the casing and bedrock was fully cemented. XLOT was carried out for several purposes such as confirming casing and cementing integrity, measuring rock stress states. To that end, we drilled additional 4 m long open hole interval to directly inject water and pressurize into the rock mass using the upper API casings. During the XLOT, flow rates and interval pressures were recorded in real time. Based on the logs we tried to analyze hydraulic conductivity of the test interval.