Journal
ASIAN JOURNAL OF WATER ENVIRONMENT AND POLLUTION
Volume 16, Issue 1, Pages 49-57Publisher
IOS PRESS
DOI: 10.3233/AJW190006
Keywords
TBM performance prediction; artificial neural network; support vector machine; Beheshtabad water conveyance tunnel
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Tunnel boring machines (TBMs) are designed to excavate underground spaces and widely used in tunneling, civil and mining projects. TBM performance prediction substantially deals with the evaluation of machine's penetration rate and the number of consumed disc cutters. There are various methods and equations to predict the TBMs performance in the literature. In this paper, we predicted the penetration rate and number of consumed disc cutters in Beheshtabad water conveyance tunneling project, one of the major water conveyance tunneling projects in Iran, using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. Results showed that both approaches are very effective but SVM yields more precise and realistic findings than ANN.
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