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...

Full description

Saved in:

Bibliographic Details
Published in:	KSCE Journal of Civil Engineering Vol. 23; no. 7; pp. 3200 - 3206
Main Authors:	Jung, Jee-Hee, Chung, Heeyoung, Kwon, Young-Sam, Lee, In-Mo
Format:	Journal Article
Language:	English
Published:	Seoul Korean Society of Civil Engineers 01.07.2019 Springer Nature B.V 대한토목학회
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!