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Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling

https://doi.org/10.1139/t10-023
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-84628-614-8openalex-snapshot
Neural Network Models
inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-snapshot
Multilayer feedforward networks are universal approximators
inferredcites →https://doi.org/10.1016/j.compgeo.2006.04.004openalex-snapshot
A numerical study of the effect of soil and grout material properties and cover depth in shield tunnelling
inferredcites →https://doi.org/10.1016/j.compgeo.2006.08.007openalex-snapshot
Tunneling performance prediction using an integrated GIS and neural network
inferredcites →https://doi.org/10.1016/j.tust.2004.02.056openalex-snapshot
Recent experience in automatic tail void grouting with soft ground tunnel boring machines
inferredcites →https://doi.org/10.1016/j.tust.2005.04.007openalex-snapshot
Estimation of air losses in compressed air tunneling using neural network
inferredcites →https://doi.org/10.1016/j.tust.2005.06.006openalex-snapshot
On the influence of face pressure, grouting pressure and TBM design in soft ground tunnelling
inferredcites →https://doi.org/10.1016/j.tust.2005.06.007openalex-snapshot
Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling
inferredcites →https://doi.org/10.1016/j.tust.2005.07.001openalex-snapshot
Prediction of tunneling-induced ground movement with the multi-layer perceptron
inferredcites →https://doi.org/10.1016/j.tust.2007.07.002openalex-snapshot
Artificial neural networks analysis of São Paulo subway tunnel settlement data
inferredcites →https://doi.org/10.1016/s0266-352x(01)00011-8openalex-snapshot
Neural network based prediction of ground surface settlements due to tunnelling
inferredcites →https://doi.org/10.1051/geotech/2013142017openalex-snapshot
Modélisation 3D du creusement de tunnel par tunnelier à front pressurisé dans les sols surconsolidés
inferredcites →https://doi.org/10.1061/(asce)0887-3801(2004)18:2(105)openalex-snapshot
Data Division for Developing Neural Networks Applied to Geotechnical Engineering
inferredcites →https://doi.org/10.1061/(asce)1090-0241(2007)133:4(445)openalex-snapshot
Describing Settlement Troughs over Twin Tunnels Using a Superposition Technique
inferredcites →https://doi.org/10.1109/2.485891openalex-snapshot
Artificial neural networks: a tutorial
inferredcites →https://doi.org/10.5555/70405.70408openalex-snapshot
Multilayer feedforward networks are universal approximators

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets
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Tunnel boring machine vibration-based deep learning for the ground identification of working faces
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Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method
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