Prediction of TBM cutterhead speed and penetration rate for high-efficiency excavation of hard rock tunnel using CNN-LSTM model with construction big data-Reference-Cited by-同舟云学术

Prediction of TBM cutterhead speed and penetration rate for high-efficiency excavation of hard rock tunnel using CNN-LSTM model with construction big data

Published:2022-01-29 Issue:3 Volume:15 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Li Long,Liu Zaobao^ORCID,Zhou Hongyuan,Zhang Jing,Shen Wanqing,Shao Jianfu

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s12517-022-09542-0.pdf

Reference47 articles.

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2. Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Space Technol 23:600–608. https://doi.org/10.1016/j.tust.2007.11.003

3. Alvarez Grima M, Babuška R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349. https://doi.org/10.1016/S0148-9062(99)00007-8

4. Bedoui A, Lazar NA (2020) Bayesian empirical likelihood for ridge and lasso regressions. Comp Stat Data Anal 145:106917. https://doi.org/10.1016/j.csda.2020.106917

5. Benato A, Oreste P (2015) Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics. Int J Rock Mech Min Sci 74:119–127. https://doi.org/10.1016/j.ijrmms.2014.12.007

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