• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.25-37
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• 2024

The objective of this study was to create intelligent rail robots that are optimized for facility management and implement digital twin systems for smart road tunnel management. An autonomous surveillance system is formed by combining the sensing platform consisting of railing robots, fixed cameras and environmental detection sensors with the digital twin data platform technology for tunnel monitoring and early fire suppression. In order to develop mobile rail robots for fire extinguishing, we also designed and manufactured robots for extinguishing & monitoring and fire extinguishing devices, and then we examined the optimization of all parts. Our next step was to build a digital twin for road tunnel management by developing continuous image display system and implementing 3D modeling. After constructing prototypes, we attempted simulations by configuring abnormal symptom scenarios, such as vehicles fires. This study's proposal proposes high-accuracy risk prediction services that will enable intelligent management of risks in the tunnel with early response at each stage, using the data collected from the intelligent rail robots and digital twin systems.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.302-314
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• 2024

Purpose: This study proposes a fall detection model based on a top-down deep learning pose estimation model to automatically determine falls of multiple workers in an underground utility tunnel, and evaluates the performance of the proposed model. Method: A model is presented that combines fall discrimination rules with the results inferred from YOLOv8-pose, one of the top-down pose estimation models, and metrics of the model are evaluated for images of standing and falling two or fewer workers in the tunnel. The same process is also conducted for a bottom-up type of pose estimation model (OpenPose). In addition, due to dependency of the falling interference of the models on worker detection by YOLOv8-pose and OpenPose, metrics of the models for fall was not only investigated, but also for person. Result: For worker detection, both YOLOv8-pose and OpenPose models have F1-score of 0.88 and 0.71, respectively. However, for fall detection, the metrics were deteriorated to 0.71 and 0.23. The results of the OpenPose based model were due to partially detected worker body, and detected workers but fail to part them correctly. Conclusion: Use of top-down type of pose estimation models would be more effective way to detect fall of workers in the underground utility tunnel, with respect to joint recognition and partition between workers.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.189-206
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• 2003

During tunnel excavation in urban area a systematic monitoring is important for the purpose of determination of support type and quantity, as well as for the control of stability of both surface structures and the tunnel itself due to the frequently, and in many cases, abruptly changing ground condition. In Austria absolute displacement monitoring methods have replaced relative displacement measurements by geodetic methods to a large extent. Prompt detection of weak ground ahead of the tunnel face as well as better adjustment of excavation and support to the geotechnical conditions is possible with the help of the improved methods of data evaluation on sites. Deformation response of the ground to excavation starts ahead of the tunnel face, therefore, the deformation and state of the tunnel advance core is the key factor of the whole deformation process after excavation. In other words, the rigidity and state of the advance core play a determining role in the stability of both surface structures and the tunnel itself. This paper presents the results from detailed three-dimensional numerical studies, exploring vertical displacements, vector orientations and extrusions on tunnel face during the progressive advancement for the shallow tunnel in various geotechnical conditions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.1
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• pp.19-28
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• 2007

Tunnel management is a time-consuming and expensive task. In particular, visual analysis of tunnel inspection often requires extended time and cost and shows problems on data gathering, storage and analysis. This study proposes a new approach to extract information for tunnel management by using a laser scanning technology. A prototype tunnel laser scanner developed was used to obtain point clouds of a railway tunnel surface. Initial processing of laser scanning data was to separate those laser pulses returned from the installations attached to tunnel liner using radiometric and geometric characteristics of laser returns. Once the laser returns from the installations were separated and removed, physically damaged parts on tunnel lining are detected. Based on the plane formed by laser scanner data, damaged parts are detected by analysis of proximity. The algorithms presented in this study successfully detect the physically damaged parts which can be verified by the digital photography of the corresponding location on the tunnel surface.

• Fire Science and Engineering
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• v.33 no.1
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• pp.85-91
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• 2019

This study evaluated the fire detection performance of an automatic fire extinguishing system for road tunnels, which combines flame wavelength detection technology with flame image detection technology. This fusion technique to improve the fire detection capability can reduce the damage caused by the fire suppression by locating the fire source in the fire and discharging the pressurized water only at the fire source. Experiments were conducted to determine the position of a fire source when a $70cm{\times}70cm$ target was placed at a distance of 15 m, 20 m, 25 m, 30 m, and 35 m, respectively, in a situation where there is a flame and smoke in a tunnel. The performance of the ultraviolet and triple wavelength infrared (IR3) sensors was attenuated due to the interference of thick smoke. In addition when the flame was blocked by thick smoke, the image sensor sensed the smoke and emitted a fire signal.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.471-476
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• 2003

The presence of voids under pavements or behind tunnel linings results in their deterioration. To detect these voids effectively by non-destructive tests, a method using radar was proposed. In this research, not only the detection of shape of voids, but also the location of reinforcing bars by radar image analysis is investigated. The experiments and image processing were conducted to detect voids and to locate reinforcing bars in or under concrete pavements (or tunnel linings) with reinforcing bars. From the results, the fundamental algorithm for tracing the reinforcing bars and voids, improving the horizontal resolution of the object image and detecting shape of objects, was verified.

• Journal of electromagnetic engineering and science
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• v.14 no.2
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• pp.79-81
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• 2014

In B-scan data measured by a pulse-type monostatic borehole radar, target signatures are seriously obscured by two clutters that differ in orientation and intensity. The primary clutter appears as a nearly constant time delay, which is caused by internal ringing between antenna and transceiver in the radar system. The secondary clutter occurs as an oblique time delay due to the guided borehole wave along the logging cable of the radar antenna. This issue led us to perform adaptive filtering processing for orientation-based clutter removal. This letter describes adaptive filtering processing consisting of a combination of edge detection, data rotation, and eigenimage filtering. We show that the hyperbolic signatures of a dormant air-filled tunnel target can be more distinctly enhanced by applying the proposed approach to the B-scan data, which are measured in a well-suited test site for underground tunnel detection.

• Geomechanics and Engineering
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• v.36 no.1
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• pp.1-8
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• 2024

We propose a new method for detecting voids behind tunnel concrete linings using the impact-echo method that is based on continuous wavelet transform (CWT) and a convolutional neural network (CNN). We first collect experimental data using the impact-echo method and then convert them into time-frequency images via CWT. We provide a CNN model trained using the converted images and experimentally confirm that our proposed model is robust. Moreover, it exhibits outstanding performance in detecting backfill voids and their status.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.549-558
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• 2021

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.