Convolutional Neural Network for Predicting the Performance of a Tunnel Boring Machine

Author:

Zhu Yan,Li Min,Nie Yonghua,Wang Ruirui,Wang Yaxu,Guo Xu

Funder

National Natural Science Foundation of China

Chongqing Municipal Key Research and Development Program of China

Joint Funds of the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Reference41 articles.

1. Efkehari A, Aalianvari A, Rostami J (2018) Influence of TBM operational parameters on optimized penetration rate in schistose rocks, a case study: Golab tunnel Lot-1, Iran. Comp concr 22:239–248. https://doi.org/10.12989/cac.2018.22.2.239

2. Zhou J, Qiu Y, Armaghani DJ, Zhang W, Li C, Zhu S et al (2021) Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques. Geosci Front 12(3):101091. https://doi.org/10.1016/j.gsf.2020.09.020

3. Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBMs. In 11th rapid excavation and tunneling conference, Boston, MA, 1993

4. Ozdemir L (1997) Development of theoretical equations for predicting tunnel borability, In Ph.D. thesis; Colorado school of Mines, CO, United States of America

5. Yagiz S (2002) Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock tunneling machines. In Ph.D. thesis; Colorado School of Mines, CO, United States of America

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