• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.427-435
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• 2009

This study deals with the analytical solution for soil-lining interaction in a deep and circular tunnel. Simple closed-form analytical solutions for thrust and moment in the circular tunnel lining due to static and seismic loadings are developed by considering the relations between displacement and interaction forces at the soil-lining interface. The interaction effect at the soil-lining interface is considered with new ratios (the normal and shear stiffness ratios). The effects of the ratios on the normalized thrust and the normalized moment are investigated.

• Tunnel and Underground Space
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• v.10 no.4
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• pp.559-565
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• 2000

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• 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.

• Magazine of korean Tunnelling and Underground Space Association
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• v.14 no.2
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• pp.31-37
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• 2012

In the circumstances being continuous the unusual weather in the world, the city of Seoul has been devastating flood damage in July 2011, because of the heavy rainfalls. Along with expensive repairs to property, thousands of flood victims occurred; it is difficult to estimate the direct and indirect economic damages in city. Recently, as a part of the flood protecting measures, there are being discussed about the deep underground flood drainage tunnel, underground regulating reservoirs, permeable pavement, infiltration facility, river improvements, diversion channel, sewer pipe and ditch improvement and so on. Therefore, it is useful to make the plan of flood protecting measures more and more cost-effective and rational methods by considering the similar flood measures and constructions in the mega cities like Seoul.

• Tunnel and Underground Space
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• v.27 no.4
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• pp.185-194
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• 2017

This technical paper is a study on the unique displacements of Shotcrete Lining at the mined tunnel during excavation period through deep consideration with real time data from monitoring instrumentations correlation with the numerical analysis to identify the rock stresses and the rock spring points at the working face of the Conventional tunnelling by the Drill and Blast, based on the geological face mapping results of the project NS2, Transmission cable tunnel project in Singapore. The created geometry of numerical model was prepared to the real mined tunnel construction site including, vertical shaft, construction adit, tunnel junction area, and 2 enlargement caverns. The convergence measurements by the monitoring instrumentation were performed during the tunnel excavation and shaft sinking construction stages to guarantee the safety of complicated underground structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Journal of the Society of Disaster Information
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• v.6 no.1
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• pp.92-119
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• 2010

As the necessity to utilize deep-placed underground spaces is increasing, we have to seriously consider the safety problems arising from the U/G spaces which is a restricted environment. Due to the higher cost of land compensation for above ground area and environmental issues, the plan to utilize deep-placed U/G spaces is currently only being established for the construction of U/G road network and GTX. However it is also expected that the U/G spaces are to be used as a living space because of the growing desires to change the above ground areas into the environmentally green spaces. Accordingly it is necessary to protect the U/G environments which is vulnerable against desasters caused by fire, explosion, flooding, terrorism, electric power failure, etc. properly. We want to introduce the principles of the Quantitative Risk Assessment(QRA) method for preparedness against the desasters arising from U/G environments, and also want to introduce an example of QRA which was implemented for the GOTTHARD tunnel which is the longest one in Europe.

• Geomechanics and Engineering
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• v.21 no.2
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• pp.95-102
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• 2020

Population concentration in urban areas has led traffic management a central issue. To mitigate traffic congestions, the government has planned to construct large-cross-section tunnels deep underground. This study focuses on estimating the damage caused to frame structures owing to tunnel excavation. When constructing a tunnel network deep underground, it is necessary to divide the main tunnel and connect the divergence tunnel to the ground surface. Ground settlement is caused by excavation of the adjacent divergence tunnel. Therefore, predicting ground settlement using diverse variables is necessary before performing damage estimation. We used the volume loss and cover-tunnel diameter ratio as the variables in this study. Applying the ground settlement values to the settlement induction device, we measured the extent of damage to frame structures due to displacement at specific points. The vertical and horizontal displacements that occur at these points were measured using preattached LVDT (Linear variable differential transformer), and the lateral strain and angular distortion were calculated using these displacements. The lateral strain and angular distortion are key parameters for structural damage estimation. A damage assessment chart comprises the "Negligible", "Very Slight Damage", "Slight Damage", "Moderate to Severe Damage", and "Severe to Very Severe Damage" categories was developed. This table was applied to steel frame and concrete frame structures for comparison.