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Machine Learning in tunnelling – Capabilities and challenges
Author(s) -
Marcher Thomas,
Erharter Georg H.,
Winkler Manuel
Publication year - 2020
Publication title -
geomechanics and tunnelling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.317
H-Index - 18
eISSN - 1865-7389
pISSN - 1865-7362
DOI - 10.1002/geot.202000001
Subject(s) - quantum tunnelling , field (mathematics) , software deployment , rock mass classification , computer science , displacement (psychology) , engineering , construction engineering , mining engineering , geotechnical engineering , software engineering , optoelectronics , psychology , physics , mathematics , pure mathematics , psychotherapist
Digitalization will change the way of gathering geological data, methods of rock classification, application of design analyses in the field of tunnelling as well as tunnel construction and maintenance processes. In recent years, a rapid increase in the successful application of digital techniques (Building Information Modelling and Machine Learning (ML)) for a variety of challenging tasks has been observed. Driven by the increasing overall amount of data combined with the easy availability of more computing power, a sharp increase in the successful deployment of techniques of ML has been seen for different tasks. ML has been introduced in many sciences and technologies and it has finally arrived in the fields of geotechnical engineering, tunnelling and engineering geology, although still not as far developed as in other disciplines. This paper focuses on the potential of ML methods for geotechnical purposes in general and tunnelling in particular. Applications such as automatic rock mass behaviour classification using data from tunnel boring machines (TBM), updating of the geological prognosis ahead of the tunnel face, data driven interpretation of 3D displacement data or fully automatic tunnel inspection will be discussed.