Prediction model of tunnel boring machine performance by ensemble neural networks
The penetration rate of a tunnel boring machine (TBM) depends on many factors ranging
from the machine design to the geological properties. Therefore it may not be possible to
capture this complex relationship in an explicit mathematical expression. In this paper, we
propose an ensemble neural network (ENN) to predict TBM performance. Based on site
data, a four-parameter ENN model for the prediction of the specific rock mass boreability
index is constructed. Such a neural-network-based model has the advantages of taking into …
from the machine design to the geological properties. Therefore it may not be possible to
capture this complex relationship in an explicit mathematical expression. In this paper, we
propose an ensemble neural network (ENN) to predict TBM performance. Based on site
data, a four-parameter ENN model for the prediction of the specific rock mass boreability
index is constructed. Such a neural-network-based model has the advantages of taking into …
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