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.
