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http://dx.doi.org/10.7843/kgs.2019.35.9.37

Rock Classification Prediction in Tunnel Excavation Using CNN  

Kim, Hayoung (Engrg. Center, Samsung C&T Corporation)
Cho, Laehun (Engrg. Center, Samsung C&T Corporation)
Kim, Kyu-Sun (Engrg. Center, Samsung C&T Corporation)
Publication Information
Journal of the Korean Geotechnical Society / v.35, no.9, 2019 , pp. 37-45 More about this Journal
Abstract
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.
Keywords
Deep Learning; VGG16; Convolutional Neural Network; Face Mapping; RMR;
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Times Cited By KSCI : 1  (Citation Analysis)
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