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Machine learning to inform tunnelling operations: recent advances and future trends

Abstract:

The proliferation of data collected by modern tunnel boring machines (TBMs) presents a substantial opportunity for the application of machine learning (ML) to support the decision making process on site with timely and meaningful information. The observational method is now well-established in geotechnical engineering and has a proven potential to save time and money relative to conventional design. ML advances the traditional observational method by employing data analysis and pattern recogn...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1680/jsmic.20.00011

Authors


More by this author
Institution:
University of Oxford
Department:
ENGINEERING SCIENCE
Sub department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1462-1401
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-5460-5089
Publisher:
ICE Publishing Publisher's website
Journal:
Proceedings of the Institution of Civil Engineers Journal website
Volume:
173
Issue:
4
Pages:
74-95
Publication date:
2020-11-24
Acceptance date:
2020-11-19
DOI:
EISSN:
1753-7789
Language:
English
Keywords:
Pubs id:
1146075
Local pid:
pubs:1146075
Deposit date:
2020-11-19

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