TY - JOUR
T1 - Potential applications of machine learning for BIM in tunnelling
AU - Erharter, Georg Hermann
AU - Weil, Jonas
AU - Tschuchnigg, Franz
AU - Marcher, Thomas
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - BIM
KW - Conventional tunneling
KW - Engineering geology
KW - Machine Learning
KW - Mechanized tunneling
KW - Rock mechanics
KW - Soil mechanics
KW - tunnelling
UR - http://www.scopus.com/inward/record.url?scp=85128752737&partnerID=8YFLogxK
U2 - 10.1002/geot.202100076
DO - 10.1002/geot.202100076
M3 - Article
SN - 1865-7362
VL - 15
SP - 216
EP - 221
JO - Geomechanics and Tunnelling
JF - Geomechanics and Tunnelling
IS - 2
ER -