• The Journal of Engineering Geology
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• v.15 no.4 s.42
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• pp.463-473
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• 2005

Because of using the same rating ranges for every rock types the RMR or the Q-system could not usually consider local geological characteristics They also could not present sufficiently the engineering anisotropy of rocks. The canonical correlation analysis was carried out with 3 kinds of face mapping data obtained from granite, sedimentary rock and phyllite in order to clarify a discrepancy between rock types. According to analysis results, as a type of rocks changes, RM factors have different influences on the total rating of RMR.

• The Journal of Engineering Geology
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• v.18 no.4
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• pp.545-553
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• 2008

Face mapping and displacement monitoring during tunnel construction are the most influential information for the stability assessment of ground and around structures. Especially, the result of face mapping and displacement analysis is essential to the excavation and support design in NATM which is based on the drilling and blasting. However, there have not been so many studies to put those useful information into practice for decision-making process during construction. The study reviewed the tunnel behaviour based on the RMR rating and displacement monitoring when the geological condition of rock mass varies inevitably. The study analysed the crown settlement using convergence equation in order to compensate the disparity induced by the location and time of measurement and found a distinct relation between the geological condition and the line of influence. As a result of analysing the various parameters related to the tunnel convergence according to the geological condition, the study suggested the basic knowledge about the relation between face mapping and displacement behaviour of 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.

• 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.19 no.1
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• pp.43-50
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• 2009

A 3-D numerical analysis was carried out to observe potential effects of orientation of inherent weak zones to tunnel behaviors and stress distributions during tunnel excavation. Weak zones used for the analysis were placed at the upper 1D part from crown, on the crown and on the center of face, using orientations derived from the 6th RMR parameter for assessment of joint orientation effect on tunnel. Mechanical properties of rock mass were derived through a in-situ displacement measurement-based back analysis. Finally, a classification chart for crown settlement with five ranks based on orientation and location of weak zones is suggested.