• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1998.10a
• /
• pp.413-420
• /
• 1998

In this study, an expert system is developed to evaluate a safety of tunnel structures. Using a dynamic finite element analysis module, this expert system predicts dynamic responses of a concrete lining surface which a transient force is applied on and estimates the condition between the concrete lining and surrounding ground. The evaluation parameter values of the module are multi-reflected wave frequency and amplitude of the dynamic responses. The multi-reflected wave frequency represents the depth of concrete lining, and the other parameter, the amplitude of the frequency, is utilized for detecting the internal flaws. A comparison of the dynamic responses between numerical and experimental model test verifies an effectiveness of this system. By this expert system, the safety of tunnel structures and the detection of internal flaws of concrete linings are estimated quantitatively.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1734-1737
• /
• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of somke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility form the fire. We need to develope a new high precision smoke detection system to keep expensive industrial facilities most reliably form fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke particles in the air. It is operated continously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities, such as power plants, underground common tunnel, main control rooms, computer rooms etc.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.128-132
• /
• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of smoke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility from the fire. We need to develope a new high precision smoke detection system to keep expensive industial facilities most reliably from fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke psrticles in the air. It is operated continuously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities,such as power plants, underground common tunnel,main control rooms,computer rooms etc.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2006.06a
• /
• pp.239-244
• /
• 2006

Borehole radar reflection surveys were carried out in the horizontal borehole to detect EDZ while constructing the tunnel for the research facility of the nuclear waste disposal in Korea. The horizontal borehole has been bored at a length of 35 m from shelter to be parallel with the tunnel which would be planed. While the tunnel has been constructing with the explosive excavation, the borehole radar reflection surveys carried out 5 times with the interval of 2 or 4 days for monitoring EDZ. The most typical change of the reflection event resulted from the face of the wall of tunnel which had been produced newly by the excavation of the tunnel daily, EDZ has been detected with constructing images of difference between two measurement stages, and also the change of EDZ through the time has been done, which is due to the generation of crack and weakening of the rock strength of the face of the tunnel's wall near previous portion of the face of a blind end of tunnel according to explosive excavation.

• Journal of the Society of Disaster Information
• /
• v.17 no.1
• /
• pp.39-52
• /
• 2021

Purpose: Recently, due to increasing usage of high-tech equipment for facility inspection, the need of verifying high-tech equipment is being emphasized. Therefore, the purpose of this paper is to develop performance evaluation methodology of tunnel scanners that inspect tunnel facilities. Method: This paper describes literature reviews regarding the performance evaluation methodology of high-tech based equipment for facility inspection. Based on these investigations and expert advisory meetings, this paper suggests a performance evaluation methodology of tunnel scanner. Result: First evaluation indicator states minimum performance standards of tunnel scanners. Second evaluation indicator is related to tunnel scanner quality. Conclusion: The performance evaluation methodology can provide reliable equipment performance catalogues, helping users to make a proper selection of equipment. Also, developers of equipment can get authorized verification of performances, preventing poor maintenance of facilities.

• Geophysics and Geophysical Exploration
• /
• v.6 no.3
• /
• pp.138-142
• /
• 2003

Crosswell seismic data were acquired in three sections crossing a tunnel of 3 different types; one was empty, another was ailed by sand, and the other was filled by rock debris. Both the P- and S-wave first arrivals were picked and the traveltime tomography was conducted to generate the P- and S- wave velocity tomograms on the all three sections. Among six tomograms, only one tomogram shows a low velocity zone that can be interpreted as a tunnel image. The tomogram is the P wave velocity image of a section that crosses an empty tunnel. The result of numerical analysis for the spatial resolution of the traveltime tomography was consistent to this finding.

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

• Journal of the Society of Disaster Information
• /
• v.19 no.3
• /
• pp.665-672
• /
• 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 Institute of Control, Robotics and Systems
• /
• v.10 no.6
• /
• pp.501-509
• /
• 2004

The frequency of car accidents is very high at the intersection. Because of the state of a traffic signal, quarrels happen after accidents. At night many cars run away after causing an accident. In this case, accident analyses have been conducted by investigating evidences such as eyewitness accounts, tire tracks, fragments of the car or collision traces of the car. But these evidences that don't have enough objectivity cause an error in judgment. In the paper, when traffic accidents happen, the traffic accident detection system that stands on the basis of images and sounds detects traffic accidents to acquire abundant evidences. And, this system transmits 10 seconds images to the traffic center through the wired net and stores images to the Smart Media Card. This can be applied to various ways such as accident management, accident DB construction, urgent rescue after awaring the accident, accident detection in tunnel and in inclement weather.

• Geophysics and Geophysical Exploration
• /
• v.13 no.1
• /
• pp.96-101
• /
• 2010

A pulse radar system has been developed recently to detect dormant underground tunnels that are deeply located at depths of hundreds of metres. To check the ability of the radar system to detect an obliquely oriented tunnel, five different borehole pairs in the tunnel test site were chosen so that the horizontal lines-of-sight cut the tunnel axis obliquely, in $15^{\circ}$ steps. The pulse radar signatures were measured over a depth range of 20 m around the centre of the air-filled tunnel. Three canonical parameters, consisting of the arrival time, attenuation, and dispersion time were extracted from the first and second peaks of the measured radar signatures. Using those parameters, the radar system can detect obliquely oriented tunnels at various angles up to 45 from the transmitter-receiver line of sight.