Potential applications of machine learning for BIM in tunnelling

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

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung


Machine Learning (ML) and Building Information Modelling (BIM) are two topics that are part of a revolutionizing transfor-mation in the construction industry – commonly referred to as digitalization. Being part of the research for artificial intelli-gence (AI), most of today’s ML applications deal with computa-tional processes that try to make sense of data. Automatic rockmass behaviour classification based on tunnel boring ma-chine (TBM) data or tunnel construction site surveillance via closed-circuit television (CCTV) analysis is an example for ap-plications 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 construc-tion industry have implemented BIM systems successfully, the development in underground engineering is currently at its be-ginning with multiple actors working towards common stan-dards 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 applica-tions: semantic enrichment and labelling, automation of techni-cal processes, knowledge derivation and online data analysis.
Seiten (von - bis)216-221
FachzeitschriftGeomechanics and Tunnelling
PublikationsstatusVeröffentlicht - Apr. 2022

ASJC Scopus subject areas

  • Geotechnik und Ingenieurgeologie
  • Tief- und Ingenieurbau


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