Capabilities and Challenges Using Machine Learning in Tunnelling

Thomas Marcher*, Georg Hermann Erharter, Paul Johannes Unterlaß

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.
Original languageEnglish
Title of host publicationTheory and Practice on Tunnel Engineering
Publication statusE-pub ahead of print - 21 May 2021

ASJC Scopus subject areas

  • Artificial Intelligence
  • Civil and Structural Engineering

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