Some Attempts for Estimating Rock Strength and Rock Mass Classification from Cutting Force and Investigation of Optimum Operation of Tunnel Boring Machines

2005 ◽  
Vol 39 (1) ◽  
pp. 25-44 ◽  
Author(s):  
K. Fukui ◽  
S. Okubo
Keyword(s):  
Rock Mass ◽  
Cutting Force ◽  
Rock Strength ◽  
Rock Mass Classification ◽  
Tunnel Boring Machines ◽  
Optimum Operation ◽  
Tunnel Boring ◽  
Mass Classification ◽  
Boring Machines

scholarly journals Rock Mass Classification of Granite by TBM Cutting Force.

2000 ◽  
Vol 116 (10) ◽  
pp. 831-838 ◽  
Author(s):  
Katsunori FUKUI ◽  
Seisuke OKUBO ◽  
Kazunori MATSUMOTO ◽  
Yoshihisa NAWA ◽  
Teruo SAKAI ◽  
...  
Keyword(s):  
Rock Mass ◽  
Cutting Force ◽  
Rock Mass Classification ◽  
Mass Classification

Rock Mass Classification Using Electrical Resistivity - an Analytical Study

2006 ◽  
Vol 321-323 ◽  
pp. 1411-1414 ◽  
Author(s):  
Jun Su Choi ◽  
Hee Hwan Ryu ◽  
In Mo Lee ◽  
Gye Chun Cho
Keyword(s):  
Electrical Resistivity ◽  
Rock Mass ◽  
Analytical Study ◽  
Rock Strength ◽  
Rock Mass Classification ◽  
Electrical Characteristics ◽  
Joint Spacing ◽  
Mass Classification ◽  
Theoretical Analyses ◽  
Q System

Geophysical prospecting using electrical resistivity is one of the principal methods for subsurface exploration. However, the majority of such application methods are restricted to coarse descriptions of underground conditions. The Q-system is commonly used as a representative rock mass classification system in modern rock engineering. In this paper, electrical resistivity is related to the Q-system through theoretical analyses. The analyses are based on Coulomb's law and Gauss' law considering electrical characteristics of constituent parameters for rock mass classification such as joint thickness, joint condition, joint spacing, intact rock strength, and RQD. The results show that there is a strong correlation between electrical resistivity and rock mass classification.

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

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.

Using Indirect Testing Methods to Quickly Acquire the Rock Strength and Rock Mass Classification in Tunnel Engineering

2020 ◽  
Vol 20 (5) ◽  
pp. 05020001
Author(s):  
Yiguo Xue ◽  
Fanmeng Kong ◽  
Shucai Li ◽  
Lewen Zhang ◽  
Binghua Zhou ◽  
...  
Keyword(s):  
Rock Mass ◽  
Rock Strength ◽  
Rock Mass Classification ◽  
Tunnel Engineering ◽  
Testing Methods ◽  
Mass Classification

Considerations to Automatic Control of Excavation Disk Cutter for Tunnel Boring Machines based on Variance Assessment

2017 ◽  
Vol 137 (1) ◽  
pp. 114-119
Author(s):  
Yoshihiro Ohnishi ◽  
Takahisa Shigematsu ◽  
Shinichi Kawamura ◽  
Noboru Oda
Keyword(s):  
Automatic Control ◽  
Tunnel Boring Machines ◽  
Disk Cutter ◽  
Tunnel Boring ◽  
Boring Machines
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