Tunnel and Underground SpaceISSN:1225-1275(Print) 2287-1748(Online)한국암반공학회

Amoun, S., Sharifzadeh, M., Shahriar, K., Rostami, J., and Azali, S.T., 2017, Evaluation of tool wear in EPB tunneling of Tehran Metro, Line 7 Expansion, Tunnelling and Underground Space Technology, 61, 233-246.

Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B., 2011, Algorithms for hyper-parameter optimization, Advances in neural information processing systems, 24.

Bruland, A., 1998, Hard rock tunnel boring : Advance rate and cutter wear. NTNU.

BS, B., 1999, 5930: 1999 Code of practice for site investigations, British Standard.

Ding, X., Xie, Y., Xue, H., and Chen, R., 2022, A new approach for developing EPB-TBM disc cutter wear prediction equations in granite stratum using backpropagation neural network, Tunnelling and Underground Space Technology, 128104654.

Elbaz, K., Shen, S.-L., Zhou, A., Yin, Z.-Y., and Lyu, H.-M., 2020, Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm, Data in Brief, 33, 106479.

Elbaz, K., Shen, S.L., Zhou, A., Yin, Z.Y., and Lyu, H.M., 2021, Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network, Engineering, 7(2), 238-251.

Frenzel, C., Käsling, H., and Thuro, K., 2008, Factors influencing disc cutter wear, Geomechanik und Tunnelbau: Geomechanik und Tunnelbau, 1(1), 55-60.

Gehring, K., 1995, Leistungs-und Verschleißprognosen im maschinellen Tunnelbau, Felsbau, 13(6), 439-448.

Kilic, K., Toriya, H., Kosugi, Y., Adachi, T., and Kawamura, Y., 2022, One-Dimensional convolutional neural network for pipe jacking EPB TBM cutter wear prediction, Applied Sciences, 12(5), 2410.

Liu, Q., Liu, J., Pan, Y., Zhang, X., Peng, X., Gong, Q., and Du, L., 2017, A wear rule and cutter life prediction model of a 20-in. TBM cutter for granite: a case study of a water conveyance tunnel in China, Rock Mechanics and Rock Engineering, 50, 1303-1320.

Loy-Benitez, J., Lee, H.K., Song, M.K., Choi, Y., and Lee, S.S., 2024, Transfer component analysis-driven domain adaptation approach for estimating the life of tunnel boring machine disc cutters, Tunnelling and Underground Space Technology, 147, 105714.

Lundberg, S.M. and Lee, S.I., 2017, A unified approach to interpreting model predictions, Advances in neural information processing systems, 30.

Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Ali, H.F.H., Hasan, A.M., Khishe, M., and Mahmud, H., 2021, Machine learning forecasting models of disc cutters life of tunnel boring machine, Automation in Construction, 128, 103779.

Rostami, J. and Ozdemir, L., 1993, A new model for performance prediction of hard rock TBMs. In: Proceedings of the rapid excavation and tunneling conference.

Su, W., Li, X., Jin, D., Yang, Y., Qin, R., and Wang, X., 2020, Analysis and prediction of TBM disc cutter wear when tunneling in hard rock strata: a case study of a metro tunnel excavation in Shenzhen, China, Wear, 446, 203190.

Wang, L., Li, H., Zhao, X., and Zhang, Q., 2017, Development of a prediction model for the wear evolution of disc cutters on rock TBM cutterhead, Tunnelling and Underground Space Technology, 67, 147-157.

Xu, D., Wang, Y., Huang, J., Liu, S., Xu, S., and Zhou, K., 2023, Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms, Tunnelling and Underground Space Technology, 131, 104852.

Yu, H., Tao, J., Qin, C., Xiao, D., Sun, H., and Liu, C., 2021, Rock mass type prediction for tunnel boring machine using a novel semi-supervised method, Measurement, 179, 109545.