• 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 Korean Tunnelling and Underground Space Association
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• v.20 no.4
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• pp.657-669
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• 2018

In recent years, the development of complex urban areas has become saturated and much attention has been focused on the development of underground space, and deep excavation is frequently performed in order to increase the utilization of underground space due to the enlargement of buildings and the high rise of buildings. Therefore, in this study, we tried to understand the behavior of the braced wall and the behavior of the tunnel adjacent to the wall according to the stiffness of the wall and the distance between the tunnel and wall. As a result of the study, the deformation of the braced wall tended to decrease with increasing the stiffness of the wall, and the axial force acting on the struts was also different according to the stiffness of braced wall. When the stiffness of the braced wall is small (2 mm), the point at which the axial force of the braces maximizes is near 0.3H of the wall. When the stiffness of the braced wall is large (5 mm), the axial force is maximum at around 0.7H of the wall. Also, the tunnel convergence occurred more clearly when the separation distance from the braced wall was closer, the stiffness of the wall was smaller, and the tunnel convergence was concentrated to the lower right part. The ground settlement due to the excavation of the ground tended to decrease as the distance between tunnel and braced wall was closer to that of the tunnel, which is considered to be influenced by the stiffness of the tunnel.

• Journal of the Korean Geotechnical Society
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• v.35 no.9
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• pp.37-45
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• 2019

Quick identification of the condition of tunnel face and optimized determination of support patterns during tunnel excavation in underground construction projects help engineers prevent tunnel collapse and safely excavate tunnels. This study investigates a CNN technique for quick determination of rock quality classification depending on the condition of tunnel face, and presents the procedure for rock quality classification using a deep learning technique and the improved method for accurate prediction. The VGG16 model developed by tens of thousands prestudied images was used for deep learning, and 1,469 tunnel face images were used to classify the five types of rock quality condition. In this study, the prediction accuracy using this technique was up to 83.9%. It is expected that this technique can be used for an error-minimizing rock quality classification system not depending on experienced professionals in rock quality rating.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.3
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• pp.189-204
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• 2015

This paper presents the results of reduced scale model tests on the effect of fault zone characteristics on the tunnel deformation behavior. A series of model tests were carried out on deep tunnels considering different fault zone orientations and offset distance. The tunnelling process was simulated in the model tests using compressed air technique. During the tests, the tunnel and ground deformation were mainly monitored while reducing the pressure inside the tunnel and the relationship between the pressure level and the tunnel deformation were established. The results indicate that for a given offset distance the tunnel behavior is influenced the most when the fault zone dips vertically while smallest influence occurs when the fault zone dips 45 degrees.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.3
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• pp.253-270
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• 2013

This paper concerns the effect of orientation and geometric characteristics of a fracture zone on the tunnel behavior using a numerical investigation. A parametric study was executed on a number of drill and blast tunnelling cases representing different fracture and tunnelling conditions using two and three dimensional finite element analyses. The variables considered include the strike and dip angle of fracture zone relative to the longitudinal tunnel axis, the width and the clearance of the fracture zone, the tunnel depth, and the initial lateral stress coefficient. The results of the analyses were examined in terms of the tunnel deformation including crown settlement, convergence, and invert heave as well as shotcrete lining stresses. The results indicate that the tunnel deformation as well as the shotcrete lining stress are strongly influenced by the orientation of the fracture zone, and that such a trend becomes more pronounced for tunnels with greater depths.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.462-469
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• 2011

The design of high-depth and large-section tunnel facilities has been increased lately. The purpose of the design is to avoid inference of existing facilities, enhance public relations and reducing the size of the station, which is advantageous for effective use of underground spaces. Diverse downtown tunnel experience, advanced excavation equipment, reinforcement methods, monitoring technologies and numerical analysis made the design possible. This paper is to introduce the design of high-depth and large-section tunnel facilities via Gimpo airport area of Deagok-Sosa railway BTL project of double-tracking.

• Tunnel and Underground Space
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• v.15 no.6 s.59
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• pp.411-424
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• 2005

Weathering is defined as a process by which surface rock, once formed in the deep ground, is broken down and altered to keep the equilibrium with the ambient environment. In this study granitic rock samples of different weathering grades were collected in the field and the microscopic observation, X-ray diffraction analysis, electron microscopic observation, chemical analysis, and rock property tests were carried out. Formation of secondary minerals, especially clay minerals, by weathering was identified and the mechanism for the change of engineering properties such as rock strength degradation was analyzed. Tunnel model test, Failure behaviour, Shallow tunnel, Unsupproted tunnel length.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.5
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• pp.823-837
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• 2018

Congestion of the city with the rapid industrial development was accelerated to build complex social infrastructure. And numerous structures have been designed and constructed in accordance with these requirements. Recently, to solve complex urban traffic, many researches of large-diameter tunnel under construction downtown are in progress. The large-diameter tunnel has been developed with a versatile double-deck of deep depth tunnel. For the safe tunnel construction, ground reinforcement methods have been developed in the weakened pillar section like as junction of tunnel. This paper focuses on evaluation of the effects of new developed ground reinforcement methods in double-deck junction. The values of reinforcement determined from the existing and developed methods were compared to each other by numerical simulation.