Dynamic prediction and multi-objective optimization on driving position of tunnel boring machine (TBM): an automated deep learning approach
Author:
Funder
National Natural Science Foundation of China
Shanghai Sailing Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11440-024-02271-6.pdf
Reference48 articles.
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3. Chai J, Wu H-Z (2023) Prevention/mitigation of natural disasters in urban areas. Smart Constr Sustain Cities 1:4. https://doi.org/10.1007/s44268-023-00002-6
4. Chauhan K, Jani S, Thakkar D, Dave R, Bhatia J, Tanwar S, Obaidat MS (2020) Automated machine learning: The new wave of machine learning, 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, pp 205–212. https://doi.org/10.1109/ICIMIA48430.2020.9074859
5. Chen C, Seo H (2023) Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with Bayesian TPE optimization and SHAP feature analysis. Acta Geotech 18:3825–3848. https://doi.org/10.1007/s11440-022-01779-z
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