Shijiazhuang Tiedao University

China State Railway Group Co., Ltd.

Lijie Du

Yalei Yang

Qingwei Li

Xun Luo

Siming Tian

Yong Zhao

Guangchun Xu

Disclosed is a method for identifying a vibration magnitude of a TBM main beam using a geological feature parameter and a tunneling feature parameter constructed based on tunnel boring machine (TBM) tunneling parameters. The method includes: collecting vibration signals of the main beam and tunneling parameters of a TBM through an in-situ tunneling test and preprocessing such data, researching a correlation law between the tunneling parameters and a vibration feature root mean square (RMS), providing two feature parameters of a field penetration index (FPI) and a cutterhead power index (TNI), establishing a multivariate regression prediction model for vibration of the TBM main beam based on the feature parameters, and verifying and analyzing the model under different surrounding rock conditions. An average relative error of prediction is 12% or within, and a quantitative relation between the vibration features of the main beam and the feature parameters of the TBM is given.

Method for identifying vibration magnitude of tunnel boring machine (TBM) main beam using geological feature parameter and tunneling feature parameter constructed based on TBM tunneling parameters

A method for real-time strength estimation, grading, and early warning of rock mass in tunnel boring machine (TBM) tunneling, and belongs to the technical field of TBM tunnel construction. The method includes the following steps: establishing a general relation model of equivalent strength Rof the TBM boring rock mass and a field penetration index (FPI); and applying the model to TBM boring construction, estimating an integrity coefficient Kof the TBM boring rock mass in real time according to boring parameters acquired by a TBM in real time, and performing grading and early warning on the TBM boring rock mass according to a given grading standard and early warning values.

Method for real-time strength estimation, grading, and early warning of rock mass in tunnel boring machine (TBM) tunneling

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Shijiazhuang, China

Lijie Du