Artificial Intelligence In Underground
Development: A Study Of TBM Performance
Author(s)
A. Benardos
Abstract
Modelling tunnel boring machine (TBM) performance is an important aspect in
tunnel operations. The use of artificial intelligence techniques such as artificial
neural networks has been recently introduced to this subject and the results from
such applications prove their potential in making accurate prognosis. This paper
presents a review of feed-forward artificial neural network (ANN) development
and furthermore it illustrates their application by the use of two cases studies
from Italian and Greek underground projects, where the TBM performance is
modelled. The results obtained show that the developed ANNs can efficiently
generalise the TBM behaviour in their respective geotechnical environment,
having a reliable, effective and consistent performance.
Keywords: TBM performance modelling, artificial neural networks.
1 Introduction
Assessing the performance of tunnelling operations is one of key data for the
overall success of the project as this issue is directly interconnected with the
financial performance of the construction works [1]. Even more important is to
estimate the tunnelling rate of tunnel boring machines (TBMs), as the flexibility
limitations these particular machines have can lead to considerable downtime.
These problems are more intense in tunnelling projects constructed in complex
geological formations [2] and especially in urban areas where the low
construction depth and the external loading from the buildings increase risk
conditions [3].
In order to assess the performance of TBMs many researchers have proposed
various methodologies [4–12] in an effort to express the penetration rate using as
Keywords
TBM performance modelling, artificial neural networks.
Related Book
Other papers in this volume
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