All Issue

2024 Vol.34, Issue 3

Technical Note

30 June 2024. pp. 185-195
Abstract
References
1

Bruland, A., 1999, Hard rock tunnel boring advance rate and cutter wear, Trondheim: Norwegian Institute of Technology.

2

Hong, J.P. and Ko, T.Y., 2023, Application of Multiple Linear Regression Analysis and Tree-Based Machine Learning Techniques for Cutter Life Index (CLI) Prediction, Tunnel and Underground Space, 33(6), 594-609.

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, D.Y., Farrokh, E., Jung, J.H., Lee, J.W., and Jee, S.H., 2017, Development of a new test method for the prediction of TBM disc cutters life, Journal of Korean Tunnelling and Underground Space Association, 19(3), 475-488.

10.9711/KTAJ.2017.19.3.475
5

Rostami, J. and Ozdemir, L., 1993, A New Model for Performance Prediction of Hard Rock TBMs. Proceedings of the Rapid Excavation and Tunneling Conference, Boston, 13-17 June 1993, 793-793.

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 :3
  • Pages :185-195
  • Received Date : 2024-05-27
  • Revised Date : 2024-06-10
  • Accepted Date : 2024-06-11