To fully leverage the advantages of mechanization and
informatization in tunnel boring machine (TBM) operations, the authors aim to
promote the advancement of tunnel construction technology toward intelligent
development. This involved exploring the deep integration of next-generation
artificial intelligence technologies, such as sensing technology, automatic
control technology, big data technology, deep learning, and machine vision,
with key operational processes,including TBM excavation, direction adjustment,
step changes, inverted arch block assembly, material transportation, and
operation status assurance. The results of this integration are summarized as
follows. (1) TBM key excavation parameter prediction algorithm was developed
with an accuracy rate exceeding 90%. The TBM intelligent step-change control algorithm, based on machine vision, achieved an image
segmentation accuracy rate of 95% and gripper shoe positioning error of ± 5 mm. (2) An automatic positioning system for inverted arch blocks
was developed, enabling real-time perception of the
spatial position and deviation during the assembly process. The system
maintains an elevation positioning deviation within ± 3
mm and a horizontal positioning deviation within ± 10
mm, reducing the number of surveyors in each work team. (3) A TBM intelligent
rail transportation system that achieves real-time
human-machine positioning, automatic switch opening and
closing, automatic obstacle avoidance, intelligent transportation planning, and
integrated scheduling and command was designed. Each locomotive formation
reduces one shunter and improves comprehensive transportation efficiency by
more than 20%. (4) Intelligent analysis and prediction algorithms were
developed to monitor and predict the trends of the hydraulic and gear oil
parameters in real time, enhancing the proactive maintenance and system
reliability.
LIU Yongsheng
,
CHEN Qiao
,
ZHANG Hepei
,
LI Shu′ao
,
LIN Chungang
,
YIN Long
,
LI Mengyu
. Open TBM Tunnel Intelligent Construction Technology[J]. Tunnel Construction, 2025
, 45(4)
: 816
-833
.
DOI: 10.3973/j.issn.2096-4498.2025.04.015
