为充分发挥敞开式TBM机械化、信息化的优势，推动隧道建造技术向智能化发展升级，探索传感技术，自动控制技术，大数据技术，深度学习、机器视觉等新一代人工智能技术与TBM掘进、调向、换步、仰拱块拼装、物料运输和运行状态保障等关键作业工序的深度融合应用。主要研究内容及结论如下： 1）开发TBM关键掘进参数预测算法，参数预测准确率达到90%以上，基于机器视觉驱动的TBM智能换步控制算法的图像分割准确率达到95%，撑靴定位误差为±5 mm。2）研发仰拱块自动定位系统，实现仰拱块拼装过程中空间位置及偏差量实时感知，高程定位偏差不超过±3 mm，水平定位偏差不超过±10 mm，每个作业班减少测量员1名。3）研发TBM智能有轨运输系统，实现人机实时定位、道岔自动开闭、障碍自动避让、运输智能规划以及一体化调度指挥，每组机车编组减少调车员1名，综合运输效率提升20%以上。4）开发液压油、齿轮油油品参数变化趋势智能分析预测算法，实时监测油品参数并预测其变化趋势。
刘永胜
,
陈桥
,
张合沛
,
李叔敖
,
林春刚
,
尹龙
,
李梦雨
. Open TBM Tunnel Intelligent Construction Technology（敞开式TBM隧道智能施工技术研究）[J]. 隧道建设, 2025
, 45(4)
: 816
-833
.
DOI: 10.3973/j.issn.2096-4498.2025.04.015
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.
