Prediction Model for Disc Cutter Wear of Tunnel Boring Machines in Sandy Cobble Strata

As the main rock breaking tool of tunnel boring machine, the wear of disc cutter is affected by geological conditions, equipment factors and tunnelling parameters when it interacts with rock. Because of the complex factors affecting the disc cutter wear, it is difficult to accurately predict the wear of disc cutter. Firstly, the rock breaking mechanism and the force of disc cutter were analyzed, a theoretical prediction model of disc cutter wear is established based on the principle of friction work. Then, The parameters in the disc cutter wear prediction model can be determined by simulation and a prediction method of disc cutter wear based on friction work is proposed. Finally, the wear prediction model of disc cutter is verified by field wear data of disc cutter. The results show that the average error between the predicted value of disc cutter and the actual wear data of construction site is 16.1%. The wear prediction model of disc cutter has good accuracy and adaptability. The research results provide an effective method for wear prediction of disc cutter, which is of great significance and engineering value for cutter replacement and construction management.

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The Effect of Disc Cutter Parameters on Rock Fragmentation in Tunnel Boring Machines

International Journal of Simulation Systems Science & Technology ◽

10.5013/ijssst.a.17.13.16 ◽

2016 ◽

Author(s):

Special Issues Editor

Keyword(s):

Rock Fragmentation ◽

Disc Cutter ◽

Tunnel Boring Machines ◽

Tunnel Boring ◽

Boring Machines

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Performance- and cutter-wear prediction for tunnel boring machines

International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts ◽

10.1016/0148-9062(96)81919-x ◽

1996 ◽

Vol 33 (6) ◽

pp. A271 ◽

Cited By ~ 1

Keyword(s):

Wear Prediction ◽

Tunnel Boring Machines ◽

Tunnel Boring ◽

Cutter Wear ◽

Boring Machines

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Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211061682 ◽

2022 ◽

pp. 095440622110616

Author(s):

Huo Junzhou ◽

Jia Guopeng ◽

Liu Bin ◽

Nie Shiwu ◽

Liang Junbo ◽

...

Keyword(s):

Rock Mass ◽

Prediction Model ◽

Real Time ◽

Prediction Method ◽

Tunnel Boring Machines ◽

Tunnel Boring ◽

Error Accumulation ◽

Boring Machines ◽

Advance Time ◽

And Control

Geological layers excavated using tunnel boring machines are buried deeply and sampled difficultly, and the geological behavior exhibits high diversity and complexity. Excavating in uncertain geology conditions bears the risks of excessive damage to the equipment and facing geologic hazards. Many scholars have used various signals to predict the advance geology conditions, but accurate prediction of these conditions in real-time and without effecting operations has not been realized yet. In this article, based on a large amount of corresponding data, an advance prediction model of the rock mass category (RMC) is formulated. First, the problem is divided into two parts, which are modeled separately to reduce the complexity of design and training. Then, the two models are combined in a pre-trained model, which is retrained to as the final prediction model to avoid the problem of error accumulation. The final model can predict the advance RMC in real-time and without affecting operations. The accuracy of the prediction model reaches 99% at an advance time of 60 min. The advance RMC can be used to guide the selection of support modes and control parameters without additional detection equipment and excavation down-time.

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