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.
LIU Yaoru
,
HOU Shaokang
,
WEI Fang
,
YU Jialin
,
HE Wei
,
CHENG Li
,
JIAO Pengcheng
. State of the Art and
Development Trends in Intelligent Construction of Tunnel Boring Machines[J]. Tunnel Construction, 2025
, 45(1)
: 21
-45
.
DOI: 10.3973/j.issn.2096-4498.2025.01.02
