Abstract

Tunnel boring machines (TBMs) have become essential for modern underground infrastructure development; however, their operation remains challenging due to complex geological variability, uncertain ground conditions and strong interactions between machine behaviour and surrounding formations. The increasing digitalization of TBM systems has generated large volumes of operational and geological data, creating new opportunities for artificial intelligence (AI) and machine learning (ML)-based intelligent tunnelling. This mini-review provides a comprehensive overview of the recent progress of AI-driven TBM technologies, covering the data foundation of intelligent tunnelling, machinelearning paradigms, geological condition identification, TBM performance prediction, safety and anomaly detection, adaptive learning, operational optimization, and digital twin-enabled closed-loop control. The review highlights how ML has evolved from standalone predictive modelling toward integrated frameworks capable of connecting real-time sensing, state estimation, decision-making and operational control. Despite significant advances, practical deployment remains limited by challenges including data scarcity and quality, model transferability across different projects and geological environments, interpretability, uncertainty quantification, computational requirements and operational reliability. Future research directions are expected to focus on adaptive and physics-informed learning, multimodal data integration, transfer learning, human-supervised autonomous control and digital twin frameworks. These developments will be critical for transforming TBMs from data-rich excavation machines into intelligent systems capable of safer, more efficient and increasingly autonomous tunnelling operations.

Keywords:Tunnel boring machine; Artificial intelligence; Machine learning; Intelligent tunnelling; Digital twin; Autonomous excavation; TBM optimization