Abstract: Tunnel boring machines (TBMs) are equipped with hundreds of sensors and information acquisition systems, enabling real-time recording of operational data that reflects construction status and working performance. This provides a valuable opportunity for data-driven research and the advancement of intelligent TBM construction. The authors review recent research achievements in TBM data-driven machine learning and summarize key developments in TBM intelligent construction as follows: (1) Using TBM 3 bid section of the Songhua river water diversion project as a case study, the operational parameters and data of the TBM are introduced. TBM control parameters and the segmentation of each tunneling cycle stage are analyzed. (2) Recent advancements in three key aspects of intelligent TBM construction are systematically summarized, including intelligent perception of tunnel surrounding rock, prediction and early warning of geological hazards, and assisted decision-making of TBM tunneling parameters. (3) Based on the current technological landscape and research progress, existing research bottlenecks are discussed and future research directions are proposed, including: utilization of multi-source data for complementary information extraction, integration of domain knowledge with data-driven approaches, and application of transfer learning across different engineering projects and working conditions.

Key words: intelligent construction of tunnel boring machine, operation data, surrounding rock perception, geological hazard prediction, tunneling parameter optimization