Rock Classification Prediction in Tunnel Excavation Using CNN

Hayoung Kim; Laehun Cho; Kyu-Sun Kim

doi:10.7843/kgs.2019.35.9.37

All Issue

2019 Vol.35, Issue 9 Preview Page

Research Article

Rock Classification Prediction in Tunnel Excavation Using CNN CNN 기법을 활용한 터널 암판정 예측기술 개발

30 September 2019. pp. 37-45

PDF XML

Abstract

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.

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

References

Barton, N., Lien, R., and Lunde, J. (1974), "Engineering Classification of Rock Masses for the Design of Tunnel Support", Rock Mechanics, Vol.6, pp.183-236.

10.1007/BF01239496

Bieniawski, Z.T. (1989), Engineering Rock Mass Classification, John Wiley & Sons, New York.

Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer, pp.206-209.

Chollet, F. (2018), Deep Learning with Python, Manning Publications.

Gardezi, S.J.S., Awais, M., Faye, I., and Meriaudeau, F. (2017), "Mammogram Classification Using Deep Learning Features", IEEE International Conference on Signal and Image Processing Applications, pp.12-14.

10.1109/ICSIPA.2017.8120660

Hubel, D.H. and Wiesel, T. N. (1959), "Receptive Fields of Single Neurons in the Cat's Striate Cortex", The Journal of Physiology, Vol.148, pp.574-591.

10.1113/jphysiol.1959.sp00630814403679

Jarrett, K., Kavukcuoglu, K., Ranzato, M., and Lecun, Y. (2009), "What is the best multi-stage architecture for object recognition?", In Proc. International Conference on Computer Vision (ICCV'09), IEEE.

10.1109/ICCV.2009.5459469

Kim, C. (2011), "Supporting the Tunnel Using Digital Photographic Mapping and Engineering Rock Classification", Journal of Korean Society for Rock Mechanics, Vol.21, No.6, pp.439-449 (in Korean).

Kim, C. and John, K. (2009), "Automatic Extraction of Fractures and Their Characteristics in Rock Masses by LIDAR System and the Split-FX Software", Journal of Korean Society for Rock Mechanics, Vol.19, No.1, pp.1-10 (in Korean).

Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998), "Gradient-based Learning Applied to Document Recognition", Proc. IEEE, pp.2278-2324.

10.1109/5.726791

Nair, V. and Hinton, G. (2010), "Rectified Linear Units Improve Restricted Boltzmann Machines", International Conference on Machine Learning.

Priest, S.D. (1993), Discontinuity Analysis for Rock Engineering, Chapman & Hall, London.

10.1007/978-94-011-1498-1

Simonyan, K. and Zisserman, A. (2015), "Very deep convolutional networks for large-scale image recognition", International Conference on Learning Representation 2015.

Information

Publisher :The Korean Geotechnical Society
Publisher(Ko) :한국지반공학회
Journal Title :Journal of the Korean Geotechnical Society
Journal Title(Ko) :한국지반공학회 논문집
Volume : 35
No :9
Pages :37-45
Received Date : 2019-07-11
Revised Date : 2019-08-15
Accepted Date : 2019-09-05
DOI :https://doi.org/10.7843/kgs.2019.35.9.37

Journal of the Korean Geotechnical Society ISSN:1229-2427(Print) 2288-646X(Online) 한국지반공학회 논문집

All Issue