为解决目前TBM掘进存在依赖于司机经验，难以对异常情况做出及时响应，导致掘进减缓或刀具磨损加剧的问题，不仅需要在不停机状态下及时准确获取掌子面围岩信息，还要在了解掌子面围岩信息的情况下实现智能辅助决策。依托北疆供水二期工程，分析围岩类别、掘进效能和掘进参数等掘进指标，基于图像识别、数据挖掘和机器学习等技术，通过岩渣图像识别、刀盘振动监测和超前地质预报实现围岩状态的实时感知；构建地质信息、掘进参数、设备与支护参数数据库，进行大数据预处理及关联分析；采用多目标智能优化算法，以掘进速度和刀具寿命为目标，对掘进参数进行优化。在此基础上，提出掘进参数、支护方案、卡机应对措施等辅助决策方法。通过TBM智能辅助掘进技术在XE隧洞试验段中的应用可知，掘进速度总体可提升15.6%，刀具寿命总体提升4.5%，且未发生因掘进参数选择不当导致掘进停滞或设备异常损坏等问题。
谭忠盛
,
邓铭江
. Research on and Application of Intelligent Auxiliary Boring Technologies for Tunnel Boring Machines in Construction of Ultra-Long Tunnels（超特长隧洞TBM智能辅助掘进技术研究及应用）[J]. 隧道建设, 2024
, 44(3)
: 442
-463
.
DOI: 10.3973/j.issn.2096-4498.2024.03.003
So far tunnel boring machine(TBM) boring still depends
heavily on the pilot′s experience and it is difficult to
respond in time to the abnormal situations during TBM boring, resulting in TBM
boring delay or aggravated cutter wearing. Therefore, it is not only necessary
to obtain the accurate information of the rock mass at the tunnel face in time
without suspending the TBM boring, but also to realize intelligent auxiliary
decisionmaking after understanding the information of
the rock mass at the tunnel face. In this paper, with the Water Supply Phase Ⅱ Project in North Xinjiang of China as the study object, 〖HJ5.2mm〗TBM boring indices such as the rock
mass classes, the TBM boring efficiency, and the TBM boring parameters are
analyzed; Based on image perception, data mining and machine learning
technologies, realtime perception of rock mass
information is realized by means of muck image perception, cutterhead vibration
monitoring and advance geological prediction; Database of geological information,
TBM boring parameters, equipment and supporting parameters is built to carry
out big data preprocessing and correlation analysis; The multiobjective intelligent optimization algorithm is adopted to optimize
the TBM boring parameters, with the TBM boring speed and the cutters′ service life as the objectives; On this basis, the intelligent
auxiliary decisionmaking for the TBM boring
parameters, supporting patterns, and suggestions on countermeasures against TBM
jamming are put forward. The application of the intelligent auxiliary TBM
boring technology in the trial sections of XE tunnel has increased the TBM
boring speed by 15.6% and the cutters′ service life by
4.5%, without suspension of TBM boring or abnormal damage to the TBMs due to
improper selection of TBM boring parameters. The research results can be
popularized and applied to similar projects to improve the TBM boring
efficiency and reduce the costs.
