PSO-based Machine Learning Methods for Predicting Ground Surface Displacement Induced by Shallow Underground Excavation Method
Authors:
Open Access
- 22 September 2023
- journal article
- research article
- Published by Elsevier in KSCE Journal of Civil Engineering
- Vol. 27 (11) , 4948-4961
Abstract
No abstract availableKeywords
This publication has 47 references indexed in Scilit:
- Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machineComputers and Geotechnics, 2013
- Tunnels in sands: the effect of size, depth and volume loss on greenfield displacementsGéotechnique, 2012
- Shallow tunnelling method (STM) for subway station construction in soft groundTunnelling and Underground Space Technology, 2012
- Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunnelingTunnelling and Underground Space Technology, 2012
- Three-Dimensional Nonlinear Analyses of a Metro Tunnel in São Paulo Porous Clay, BrazilJournal of Geotechnical and Geoenvironmental Engineering, 2011
- Artificial neural network application for the prediction of ground surface movements induced by shield tunnellingCanadian Geotechnical Journal, 2010
- Artificial neural networks analysis of São Paulo subway tunnel settlement dataTunnelling and Underground Space Technology, 2008
- Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunnelingTunnelling and Underground Space Technology, 2006
- Neural-network-based regression model of ground surface settlement induced by deep excavationAutomation in Construction, 2004
- Neural network based prediction of ground surface settlements due to tunnellingComputers and Geotechnics, 2001