• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.551-557
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• 2022

Geotechnical risk factors encountered in intermediate-depth underground tunnel construction are diverse, and the types and standards of risk factors are different according to the depth and regional geological characteristics of Korea. In order to understand the effects of geological characteristics and geologic structure on safety, which show various porous characteristics of urban underground complex ground, the risk factors of intermediate-depth rock mass in Korea were analyzed based on domestic and foreign cases. As a result of the study, seven categories affecting the stability of the intermediate-depth tunneling, namely, geologic structure, rock characteristics, hydrogeology, overburden, high stress, ground characteristics and artificial structures, and about 22 risk factors were derived. We present the risk criteria and interval values for risk evaluation of faults, folds, dikes, and rocks that have the greatest influence among risk factors. Criteria and interval values for other risk factors are under study.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.113-126
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• 2024

Shotcrete lining is an important material for the stability of tunnels in NATM tunnels. However, stressmeters for stress measurements of shotcrete lining are installed in the field without performance verification because of a lack of research on methods, procedures, regulations, and reliability of measurement equipment. To solve this problem, all shotcrete stressmeters currently used in Korea were investigated. For each stressmeter, external inspection and structural and functional inspection were performed to identify defects and problems in devices. For this purpose, a shotcrete stressmeter performance test device capable of load loading in stages was developed and obtained KOLAS certification. Using the device, stressmeter performance tests were conducted. Structural problems of integrated- and cell-type shotcrete stressmeters were identified through concrete mold tests, and improvement plans and performance verification procedures were suggested. The results of this study are expected to contribute to the preparation of regulations for the performance verification of shotcrete stressmeters and the selection of measuring instruments in the field in the future.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8879-8888
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• 2015

When the disaster like earthquake in urban area occur, due to the collapse accidents for subway, tunnel space with buildings or underground area, enormous property and human damage are happened. Specially, since it is difficult to identify survived status of humans within collapsed debris and accurately buried locations of the humans, inputs of considerable time and manpower for rescuing them are required. Besides, secondary damage can be occurred by additional collapses. The aim of this study is to propose a stochastic location positioning method that enables to provide aid information by determining locations of mobile devices for buried persons in 2-D plane using wireless communication technologies. This study selected a detection method for buried persons based on Wi-Fi signal, and identified characteristics of signal strengths by distance unit. Using these methods, a stochastic location detection model in 2-D plane was built. It is expected that this technology will be utilized as a core technology that can protects safety and human life of the public by providing data for rescuing quickly buried persons in cases of national disasters for future.

• Tunnel and Underground Space
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• v.22 no.1
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• pp.69-76
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• 2012

Theoretical backgrounds on the experimental methods of explosive welding, explosive forming and shock consolidation of powders are introduced. Explosive welding experiments of titanium (Ti) and stainless steel (SUS 304) plate were carried out. It was revealed that a series of waves of metal jet are generated in the contact surface between both materials; and that the optimal collision velocity and collision angle is about 2,100~2,800 m/s and $15{\sim}20^{\circ}$, respectively. Also, explosive forming experiments of Al plate were performed and compared to a conventional press forming method. The results confirmed that the shock-loaded Al plate has a larger curvature deformation than those made using conventional press forming. For shock consolidation of powders, the propagation behaviors of a detonation wave and underwater shock wave generated by explosion of an explosive are investigated by means of numerical calculation. The results revealed that the generation and convergence of reflected waves occur at the wall and center position of water column, and also the peak pressure of the converged reflected waves was 20 GPa which exceeds the detonation pressure. As results from the consolidation experiments of metal/ceramic powders ($Fe_{11.2}La_2O_3Co_{0.7}Si_{1.1}$), shock-consolidated $Fe_{11.2}La_2O_3Co_{0.7}Si_{1.1}$ bulk without cracks was successfully obtained by adapting the suggested water container and strong bonding between powder particles was confirmed through microscopic observations.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.535-541
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• 2020

A conventional elastic material replacement and performance evaluation are very complicated and time-consuming, and it is difficult to know when to replace the elastic material in advance. By comparing with the product limit and the functional limit, the necessity of elastic material replacement and the improvement of track support stiffness according to replacement can be immediately demonstrated based on experimental data. Using an evaluation system of track support stiffness, the performance evaluation data for elastic materials obtained through field tests using software for track support stiffness is integrated and managed on the administrator's computer. Therefore, the replacement plan is established and maintenance history is managed by identifying the replacement time and location of elastic materials. It is possible to evaluate the performance and condition of the elastic material at the various points during the working time of the track inspection and the track performance (track support stiffness) and durability of the elastic material (aging level, spring stiffness variation rate, etc.) at the operation condition. The elastic material could be replaced timely, and the deterioration of the elastic material can be continuously monitored.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.313-321
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• 2017

Traffic accidents in the country have steadily increased. Although IOT technologies have been applied so as to prevent the primary accident, practical solutions to prevent the secondary accident have not been suggested. A general guideline is simply recommended. In this paper, utilizing existing communication technology, we implement a proposed model and its simulation to prevent the secondary accident. When it is possible for a driver to secure visibility, the secondary accident can be prevented; In areas like tunnel, mountain terrain, and curve road with heavy traffic, where the driver has difficulty in securing the visibility, the secondary accident rates after the primary accident have been increasing. Therefore, we implement an accident prevention system that determines the primary accident utilizing sensor technology and prevents the secondary accident communicating through V2V or V2I. After the simulation, we found that the proposed model and the existing model made no difference with regard to the secondary accident rates when the visibility of the driver is secured; With the application of the proposed model, however, the accident rates decreased for 3-7 percent even though the visibility and communication were not secured.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.669-675
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• 2020

In this study, 3D analysis was compared in evaluating the track deformation of subway structures during adjacent excavation. For the 3D analysis model, the boundary conditions of the tunnel model and the application level of the ground water were analyzed as variables. As the result of the effects of track irregularity using the 3D model, the analysis model considering the site ground water level instead of the design values and changing the constraint of the boundary condition is more reasonable. In addition, the influence of track irregularity due to the boundary condition and load condition of the analytical model is more obvious in the factors directly affected by the longitudinal relative displacement of the rail, such as alignment, cross level and gauge irregularity. Therefore, the evaluation on track stability according to adjacent excavation work was appropriate to analysed the longitudinal deformation of the track by using 3D model that could be investigate the deformation of rail. In addition, the boundary condition and load condition(ground water level) of the numerical model was important for accurate analysis results.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.725-735
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• 2022

This study analyzed how the literary works embodying nurses accommodate various discourses about nurses and reproduce them in literature. The subject of analysis is a Korean modern novel featuring a nurse, and 29 works were selected from 1927 to 2016. The analysis method is the content analysis of the novel among the qualitative research methods. The unit of analysis adopts a narrative or dialogue that deals with the image of a nurse in a novel as a unit of analysis, The image determining factors of previous studies were integrated and categorized into 4 types and considered. As a result of analyzing the image of a nurse in the novel, First, traditional image types include Lee Kwang-soo 『Love』, Kim Eui-jung 『Doctor Han』, Jo Jung-rae 『Han River』, Gong Ji-young's 「Field of Stars」, Baek Min-seok 「Poor Little Hans」 Second, social image types include Kang Kyung-ae 「Dark」, Kim Kyung-wook 「Heaven's Gate」, Choi Jeong-hee 「Cheonmaek」 Third, professional image types include Lee Cheong-jun 「Mr. Jo Man-deuk」, 「Discharge」, Choi In-hoon 『The Square』, Kim Yeon-soo 「The Night in the Tunnel Where I Listened to Jusaeng Tudipini」, Jeong Se-lang 『Public health teacher Ahn Eun-young』and Fourth, personal image types include Choi In-ho 「Apprentice Patient」, Kim Jeong-han 「The Third Ward」, Eun Hee-kyung 『Minor League』, Hoon Kim 「Hwajang」, Ha Seong-ran 「The Joy of Eating」, Kim Ji-yeon 「Hippocrates Love Song」, Park Kyung-ri 「Era of Distrust」, Jeong Mi-kyung 「The Lady of Arsenal」 typed as. Through the image of the nurse in the novel, the implications of the novel for human care were discussed.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.685-690
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• 2023

It was analyzed that the volume of deep excavation works adjacent to existing underground structures is increasing according to the population growth and density of cities. Currently, many underground structures and tracks are damaged by external factors, and the cause is analyzed based on the measurement results in the tunnel, and measurements are being made for post-processing, not for prevention. The purpose of this study is to analyze the effect on the deformation of the structure due to the excavation work adjacent to the urban railway track in use. In addition, the safety of structures is evaluated through machine learning techniques for displacement of structures before damage and destruction of underground structures and tracks due to external factors. As a result of the analysis, it was analyzed that the model suitable for predicting the structure management standard value time in the analyzed dataset was a polynomial regression machine. Since it may be limited to the data applied in this study, future research is needed to increase the diversity of structural conditions and the amount of data.