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
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Beschreibung
Zusammenfassung: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 acquisition device is required. Ground type classifications and machine data normalization methods are introduced to maintain the consistency of the measured data and improve prediction accuracy. The efficacy of the proposed model is demonstrated by its 96% accuracy in predicting ground type one ring ahead of the tunnel face.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-019-1460-9