Potential applications of machine learning for BIM in tunnelling

• Published in: Geomechanik und Tunnelbau Vol. 15; no. 2; pp. 216 - 221
• Main Authors: Erharter, Georg H., Weil, Jonas, Tschuchnigg, Franz, Marcher, Thomas
• Format: Journal Article
• Language: English
• Published: Berlin Ernst & Sohn GmbH 01.04.2022; Wiley Subscription Services, Inc
• Subjects: Artificial intelligence; Automation; BIM; Bodenmechanik; Boring machines; Building management systems; Closed circuit television; Computer applications; Construction; Construction industry; Construction sites; Conventional tunneling; Data analysis; Data exchange; Digitization; Drilling & boring machinery; Engineering geology; Felsmechanik; Fields; Ingenieurgeologie; Konventioneller Vortrieb; Labeling; Learning algorithms; Learning behaviour; Machine Learning; Maschineller Vortrieb; Mechanized tunneling; Potential fields; Rock mechanics; Semantics; Soil mechanics; Storage; Television; Tunnel construction; Tunneling; tunnelling; Tunnels; Underground construction
• ISSN: 1865-7362; 1865-7389
• DOI: 10.1002/geot.202100076

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.