• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1845-1850
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• 2003

The underground utility tunnels are important facility as a mainstay of country because of communication developments. The communication and electrical duct banks as well as various utility lines for urban life are installed in the underground utility tunnel systems. If a fire breaks out in this life-line tunnel, the function of the city will be discontinued and the huge damages are occurred. In order to improve the safety of life-line tunnel systems and the fire detection, the behaviors of the fire-induced smoke flow and temperature distribution are investigated. In this study we assumed that the fire is occurred at the contact or connection points of cable. Numerical calculations are carried out using different velocity of ventilation in utility tunnel. The fire source is modeled as a volumetric heat source. Three-dimensional flow and thermal characteristics in the underground tunnel are solved by means of FVM (Finite Volume Method) using SIMPLE algorithm and standard ${\kappa}-{\varepsilon}$ model for Reynolds stress terms. The numerical results of the fire-induced flow characteristics in an underground utility tunnel with different velocity of ventilation are graphically prepared and discussed.

• Journal of computational fluids engineering
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• v.10 no.1
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• pp.94-98
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• 2005

Accurate prediction of the fire-induced air velocity, temperature and smoke flow in underground utility tunnel becomes more important for the optimization of design and placement of heat and smoke detectors. In order to improve the safety of underground utility tunnel systems, the behaviors of fire-induced smoke flow and temperature distributions are investigated. Especially, two different cross-sectional shapes of tunnel, such as rectangular and circular types are modeled. Also, fire source is modeled as a volumetric heat source. Three-dimensional thermal-flow characteristics in an underground tunnel are solved by means of FVM using SIMPLE algorithm. The effects of shape geometry on the fire-induced flow characteristics are graphically depicted. It is desirable that heat and smoke detectors are installed on the cables and the top of the wall.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.205-213
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• 2024

In the seismic evaluation of underground utility tunnels, selecting an analytical method is critical to estimating reasonable seismic responses. In simplified pseudo-static analysis methods widely applied to typical seismic design and evaluation of underground tunnels in practice, it is essential to check whether the methods provide valid results for cut-and-cover tunnels buried in shallow to medium depth. The differences between the two simplified pseudo-static methods are discussed in this study, and the analysis results are compared to those obtained from FLAC models. In addition to the analysis methods, seismic site classification, overburden soil depth, and sectional configuration are considered variables to examine their effects on the seismic response of underground utility tunnels. Based on the analysis results, the characteristics derived from the concepts and details of each simplified model are discussed. Also, general observations are made for the application of simplified analysis methods.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.225-226
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• 2003

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.665-672
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• 2023

Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.922-929
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• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.29-39
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• 2017

In this research, thermal design data such as heat transfer coefficient on the wall surface required for ventilation system design which is to prevent the temperature rise in the underground utility tunnel that three sides are adjoined with the ground was investigated in numerical analalysis. The numerical model has been devised including the tunnel lining of the underground utility tunnel in order to take account for the heat transfer in the tunnel walls. The air temperature in the tunnel, wall temperature, and the heating value through the wall based on heating value(117~468 kW/km) of the power cable installed in the tunnel and the wind speed in the tunnel(0.5~4.0 m/s) were calculated by CFD simulation. In addition, the wall heat transfer coefficient was computed from the results analysis, and the limit distance used to keep the air temperature in the tunnel stable was examined through the research. The convective heat transfer coefficient at the wall surface shows unstable pattern at the inlet area. However, it converges to a constant value beyond approximately 100 meter. The tunnel wall heat transfer coefficient is $3.1{\sim}9.16W/m^2^{\circ}C$ depending on the wind speed, and following is the dimensionless number:$Nu=1.081Re^{0.4927}({\mu}/{\mu}_w)^{0.14}$. This study has suggested the prediction model of temperature in the tunnel based on the thermal resistance analysis technique, and it is appraised that deviation can be used in the range of 3% estimation.

• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.29-43
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• 2022

Various underground facilities are being constructed to improve the urban environment. Therefore, it is more necessary than ever to reasonably evaluate the seismic response of underground utility tunnels, playing a significant part in urban infrastructure. In this study, the major features and differences of two types of existing pseudo-static analysis methods are reviewed. Each method uses a simplified 2D frame model to represent the seismic behavior of underground structures. Applying each method to a one-barrel rectangular utility tunnel in Korea, the suitability in predicting seismic responses, especially the racking deformation of the tunnel, is examined. In addition, several precautions and suggestions are provided in this study against the inattentive application of the methods to seismic evaluation of underground structures.