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

TLDR

Modern tunnel‑boring machines generate abundant data, offering a substantial opportunity for machine learning to enhance on‑site decision‑making, building on the established observational method that saves time and money, and leveraging data analysis and pattern recognition to model the underlying physics. The paper reviews recent advances and applications of machine learning to inform tunnelling construction operations, aiming to promote industry uptake. The authors conduct a comprehensive review of recent machine‑learning advances and applications in tunnelling construction operations. The review identifies four key ML applications in tunnelling—TBM performance prediction, settlement prediction, geological forecasting, and cutterhead design optimisation—and outlines research trends and future directions.

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.