Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review

Shan, F; He, X; Xu, H; Armaghani, DJ; Sheng, D

Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review

Shan, F

He, X

Xu, H

Armaghani, DJ Sheng, D

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Eng, 2023, 4, (2), pp. 1516-1535
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Shan, F https://orcid.org/0000-0001-7497-6768
dc.contributor.author	He, X https://orcid.org/0000-0001-5336-3663
dc.contributor.author	Xu, H https://orcid.org/0000-0002-6556-8831
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2024-03-26T04:46:14Z
dc.date.available	2024-03-26T04:46:14Z
dc.date.issued	2023-06-01
dc.identifier.citation	Eng, 2023, 4, (2), pp. 1516-1535
dc.identifier.issn	2673-4117
dc.identifier.issn	2673-4117
dc.identifier.uri	http://hdl.handle.net/10453/177184
dc.description.abstract	Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Eng
dc.relation.isbasedon	10.3390/eng4020087
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-26T04:46:13Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	2

Abstract:

Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.

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http://hdl.handle.net/10453/177184