• Journal of Korean Tunnelling and Underground Space Association
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• v.7 no.1
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• pp.89-96
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• 2005

Due to fast development of digital equipments, various information techniques have been applied to the tunneling and a decision aid system based on IT has also been used during excavation stage. A PDA based informative tunneling method is, therefore, studied in this paper and the decision aids for tunneling using digital face mapping data as well as geologic information in terms of digital data is developed. For this, wireless network, mobile computer, CDMA and digital camera have been combined to generate the digital map of the tunnel face and reinforcement or excavation pattern can be estimated based on digitalized geologic conditions. Future studies will be concentrated on the enhancement of the PDA S/W so that reinforcement method as well as the amount of reinforcements can also be stored in the same DB. Furthermore, field application of the S/W will be undertaken and a virtual reality technique will also be introduced to visualize all the tunneling work on the computer monitor.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Tunnel and Underground Space
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• v.21 no.6
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• pp.439-449
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• 2011

The characteristics of rock fractures for engineering rock classification are investigated by analyzing three dimensional point cloud generated from adjusted digital images of a tunnel face during construction and the tunnel is reinforced based on the supporting pattern suggested by the RMR and the Q system using parameters extracted from those images. As results, it is possible saving time required from face mapping to tunnel reinforcing work, enhancing safety during face mapping work in tunnels and reliability of both the mapping information and selecting supporting pattern by storing the files of digital images and related information which can be checked again, if necessary sometime in the future.

• The Journal of Engineering Geology
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• v.23 no.1
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• pp.67-77
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• 2013

We conducted a Tunnel Seismic Prediction (TSP) survey in a spillway tunnel at Juam Dam to predict the locations of major discontinuities ahead of the tunnel face. We compared the results of the TSP survey with those from pre-construction inspections (including a surface resistivity survey and borehole investigations) as well as with direct tunnel-face mapping during excavation. The TSP method predicted the locations of major fracture zones that were unnoticed in the pre-construction inspections. The reinforcement patterns planned on the basis of pre-construction inspections were changed on the basis of the TSP results. The results demonstrate that TSP surveys are a cost-effective and reliably accurate method of predicting the locations of fracture zones. Although the TSP method has some limitations, these results suggest that the method is generally useful for predicting geological conditions prior to tunnel face construction.