PSO-based Machine Learning Methods for Predicting Ground Surface Displacement Induced by Shallow Underground Excavation Method
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12205-023-0121-1.pdf
Reference55 articles.
1. Boubou R, Emeriault F, Kastner R (2010) Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling. Canadian Geotechnical Journal 47:1214–1233, DOI: https://doi.org/10.1139/t10-023
2. Chen RP, Zhang P, Kang X, Zhong ZQ, Liu Y, Wu HN (2019) Prediction of maximum surface settlement caused by EPB shield tunneling with ANN methods. Soils and Foundations 59(2):284–295, DOI: https://doi.org/10.1016/j.sandf.2018.11.005
3. Fang Q, Liu X, Zhang DL, Lou HC (2017) Shallow tunnel construction with irregular surface topography using cross diaphragm method. Tunnelling and Underground Space Technology 68:11–21, DOI: https://doi.org/10.1016/j.tust.2017.05.015
4. Fang Q, Zhang DL, Wong LNY (2012) Shallow tunnelling method (STM) for subway station construction in soft ground. Tunnelling and Underground Space Technology 29:10–30, DOI: https://doi.org/10.1016/j.tust.2011.12.007
5. Feng XD, Jimenez R (2015) Predicting tunnel squeezing with incomplete data using Bayesian networks. Engineering Geology 195:214–224, DOI: https://doi.org/10.1016/j.enggeo.2015.06.017
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