Machine Learning in tunnelling – Capabilities and challenges

Thomas Marcher*, Georg Hermann Erharter, Manuel Winkler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)191-198
Number of pages8
JournalGeomechanics and Tunnelling
Issue number2
Publication statusPublished - 1 Apr 2020


  • Automatic classification
  • Big Data
  • Classification in Tunneling
  • Digitalization in Tunneling
  • Machine learning
  • Machine Learning
  • NATM
  • TBM tunnelling

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Application


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