Rock mass type prediction for tunnel boring machine using a novel semi-supervised method

•A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor and a feature classifier.•Geological feature extractor and classifier are obtained based on SSAE and DNN, respectively.•A set of data preproce...

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Veröffentlicht in:	Measurement : journal of the International Measurement Confederation Jg. 179; S. 109545
Hauptverfasser:	Yu, Honggan, Tao, Jianfeng, Qin, Chengjin, Xiao, Dengyu, Sun, Hao, Liu, Chengliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Elsevier Ltd 01.07.2021 Elsevier Science Ltd
Schlagworte:	Artificial neural networks Boring machines Data preprocessing Decision trees Feature extraction Geology Neural networks Onsite Prediction models Rock mass type prediction Rock masses Rocks Semi-supervised learning Stacked sparse autoencoder Subway tunnels Support vector machines Tunnel boring machine Tunnel construction Tunnels Underground construction Tunnel boring machine Rock mass type prediction Stacked sparse autoencoder Semi-supervised learning Data preprocessing
ISSN:	0263-2241, 1873-412X
Online-Zugang:	Volltext
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