Potential applications of machine learning for BIM in tunnelling

Georg Hermann Erharter*, Jonas Weil, Franz Tschuchnigg, Thomas Marcher

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Machine Learning (ML) and Building Information Modelling (BIM) are two topics that are part of a revolutionizing transformation in the construction industry – commonly referred to as digitalization. Being part of the research for artificial intelligence (AI), most of today's ML applications deal with computational processes that try to make sense of data. Automatic rockmass behaviour classification based on tunnel boring machine (TBM) data or tunnel construction site surveillance via closed-circuit television (CCTV) analysis is an example for applications of ML in tunnelling. BIM describes a new type of planning, including model-based collaboration and information exchange, which requires well-organized storage and handling of data – a precondition and valuable source for any automated analysis method like ML. While other sectors of the construction industry have implemented BIM systems successfully, the development in underground engineering is currently at its beginning with multiple actors working towards common standards for semantics, data exchange formats, etc. This article seeks to combine the two fields by giving an overview of the two topics and then points out four potential fields of applications: semantic enrichment and labelling, automation of technical processes, knowledge derivation and online data analysis.

Original languageEnglish
Pages (from-to)216-221
Number of pages6
JournalGeomechanics and Tunnelling
Issue number2
Publication statusPublished - Apr 2022


  • BIM
  • Conventional tunneling
  • Engineering geology
  • Machine Learning
  • Mechanized tunneling
  • Rock mechanics
  • Soil mechanics
  • tunnelling

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Civil and Structural Engineering


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