A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition

Volume: 165, Pages: 108353 - 108353
Published: Feb 1, 2022
Abstract
Accurately acquiring the geological information of the tunnel face will help to set the optimal operational parameters, so that the shield machine can achieve better tunneling performance. The design of the shield machine prevents the operator from observing the surrounding environment directly, and the soft methods which can utilize machine parameters to recognize geological conditions indirectly are becoming a research hotspot. However,...
Paper Details
Title
A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition
Published Date
Feb 1, 2022
Volume
165
Pages
108353 - 108353
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