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2024 Vol.34, Issue 6 Preview Page

Original Article

31 December 2024. pp. 722-734
Abstract
References
1

Jeong, H., Kim, M., Lee, M., and Jeon, S., 2019, Analysis of Advanced Rate and Downtime of a Shield TBM Encountering Mixed Ground and Fault Zone: A Case Study. Tunnel and Underground Space, 29(6), 394-406.

2

Kang, T.H., Choi, S.W., Lee, C., and Chang, S.H., 2020, A Study on Prediction of EPB shield TBM Advance Rate using Machine Learning Technique and TBM Construction Information, Tunnel and Underground Space, 30(6), 540-550.

3

Kang, T.H., Choi, S.W., Lee, C., and Chang, S.H., 2022, A study on the prediction of disc cutter wear using TBM data and machine learning algorithm, Tunnel and Underground Space, 32(6), 502-517.

4

Kim, T.H., Kwak, N.S., Kim, T.K., Jung, S., and Ko, T.Y., 2021, A TBM data-based ground prediction using deep neural network, Journal of Korean Tunnelling and Underground Space Association, 23(1), 13-24.

5

Mokhtari, S., and Mooney, M.A.,2020, Predicting EPBM advance rate performance using support vector regression modeling, Tunnelling and Underground Space Technology, 104, 103520.

10.1016/j.tust.2020.103520
Information
  • Publisher :Korean Society for Rock Mechanics and Rock Engineering
  • Publisher(Ko) :한국암반공학회
  • Journal Title :Tunnel and Underground Space
  • Journal Title(Ko) :터널과 지하공간
  • Volume : 34
  • No :6
  • Pages :722-734
  • Received Date : 2024-11-25
  • Revised Date : 2024-12-02
  • Accepted Date : 2024-12-03