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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data
Abstract 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.
An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data
Abstract 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.
An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data
Jung, Jee-Hee (Autor:in) / Chung, Heeyoung (Autor:in) / Kwon, Young-Sam (Autor:in) / Lee, In-Mo (Autor:in)
KSCE Journal of Civil Engineering ; 23 ; 3200-3206
27.05.2019
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data
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