Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Rock Classification Prediction in Tunnel Excavation Using CNN

CNN 기법을 활용한 터널 암판정 예측기술 개발

  • 김하영 (삼성물산(주) 건설부문 ENG센터) ;
  • 조래훈 (삼성물산(주) 건설부문 ENG센터) ;
  • 김규선 (삼성물산(주) 건설부문 ENG센터)
  • Received : 2019.07.11
  • Accepted : 2019.09.05
  • Published : 2019.09.30
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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.

터널 굴착 시 신속한 막장면 상태 파악 및 적절한 지보패턴 결정은 터널 붕락사고의 예방 및 안정적인 굴진에 매우 중요하다. 본 연구에서는 딥러닝 기법을 활용하여 막장면 상태에 따른 암반상태 분류를 신속하게 결정할 수 있는 기술을 개발하였으며, CNN 기법을 이용한 암반상태 분류방법 및 예측 정확도 개선 방법 등을 제시하고 있다. 수 만개의 이미지가 사전 학습된 VGG16 모델을 알고리즘으로 적용하였고, 1,469개의 터널 막장면 이미지에 대한 학습을 통하여 5개 등급으로 암반상태를 분류하였다. 본 연구에서의 예측 정확도는 최대 83.9% 수준을 나타내었으며, 향후 추가적인 이미지 축적을 통해 암반상태 평가자에 따른 편차를 줄인 객관적이고 정량적 암반상태 분류방법으로 활용 가능할 것으로 판단된다.

Keywords

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