Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

Author:

Liu Zaobao,Wang Yongchen,Li Long,Fang Xingli,Wang Junze

Publisher

Springer Science and Business Media LLC

Subject

Architecture,Civil and Structural Engineering

Reference52 articles.

1. Qi M. Promoting TBM in tunnel construction in China. Tunnel Construction, 2014, 34(11): 1019–1023 (in Chinese)

2. Shang Y, Yang Z, Qi Z, Sun Y, Shi Y, Yuan G. Retrospective analysis of TBM accidents from its poor flexibility to complicated geological conditions. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(12): 2404–2411 (in Chinese)

3. Li L, Liu Z, Zhou H, Qi W, Zha W. Weighted voting model for advanced intelligent perception of tunnel faults based on TBM rock-machine information. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(S2): 3401–3411 (in Chinese)

4. Liu Q, Liu J, Pan Y, Kong X. Research advances of tunnel boring machine performance prediction models for hard rock. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(S1): 2766–2786 (in Chinese)

5. Khademi F, Akbari M, Jamal S M, Nikoo M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 2017, 11(1): 90–99

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