Potential applications of machine learning for BIM in tunnelling

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 dea...

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Bibliographic Details
Published in:Geomechanik und Tunnelbau 2022-04, Vol.15 (2), p.216-221
Main Authors: Erharter, Georg H., Weil, Jonas, Tschuchnigg, Franz, Marcher, Thomas
Format: Article
Language:English
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
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Summary: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.
ISSN:1865-7362
1865-7389
DOI:10.1002/geot.202100076