An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data

This paper presents an artificial neural network (ANN) model that predicts ground conditions ahead of a tunnel face by using shield tunnel boring machine (TBM) data obtained during the tunneling operation. The primary advantage of the proposed technique is that, by using TBM data, no additional data...

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Veröffentlicht in:	KSCE Journal of Civil Engineering Jg. 23; H. 7; S. 3200 - 3206
Hauptverfasser:	Jung, Jee-Hee, Chung, Heeyoung, Kwon, Young-Sam, Lee, In-Mo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Seoul Korean Society of Civil Engineers 01.07.2019 Springer Nature B.V 대한토목학회
Schlagworte:	Accuracy Algorithms Architectural engineering Artificial neural networks Back propagation Boring machines Civil Engineering Data Data acquisition Disclaimers Drilling & boring machinery Engineering Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Mathematical models Methods Model accuracy Neural networks Predictions Trends Tunnel construction Tunnel Engineering Tunneling shields Tunnels 토목공학 artificial neural network (ANN) tunnel boring machine (TBM) ground types backpropagation (BP) algorithm tunnel face TBM data ground condition prediction
ISSN:	1226-7988, 1976-3808
Online-Zugang:	Volltext
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