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

Brian B. Sheil, Stephen K. Suryasentana, Michael A. Mooney, Hehua Zhu

Research output: Contribution to journalArticlepeer-review

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 recognition techniques, predicated on the assumption of the presence of enough data to describe the physics of the modelled system. This paper presents a comprehensive review of recent
advances and applications of ML to inform tunnelling construction operations with a view to increasing their potential for uptake by industry practitioners. This review has identified four main applications of ML to inform tunnelling – namely, TBM performance prediction, tunnelling-induced settlement prediction, geological forecasting and cutterhead design optimisation. The paper concludes by summarising research trends and suggesting directions for future research for ML in the tunnelling space.
Original languageEnglish
Number of pages22
JournalProceedings of the Institution of Civil Engineers – Smart Infrastructure and Construction
Early online date24 Nov 2020
DOIs
Publication statusE-pub ahead of print - 24 Nov 2020

Keywords

  • tunnel boring machines
  • machine learning
  • decision making
  • cutterhead design

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