Tunnel Boring Machine Performance as a Function of Local Geology

Tarkoy, P J

doi:10.2113/gseegeosci.xviii.2.169

Cited by 2 publications

(1 citation statement)

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“…Subsequently, encountering ground conditions different from the TBM's working envelope, affect the achieved tunnelling rate [21] and can give rise to claims. Thus, the model considers the geological setting to be the most dominant factor for the TBM performance, as many researchers have also noted [22,8,10], and all possible problems and downtime are a direct effect of the geotechnical conditions. The selection of the parameters used in the model was made having in mind their capability to credibly represent the ground behaviour, hydrogeological environment and site-specific conditions [23].…”

Section: Case Study 2 -Athens Metro Tunnelmentioning

confidence: 99%

Artificial intelligence in underground development: a study of TBM performance

Benardos¹

2008

Underground Spaces I

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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.

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