Rock Mass Classification of Chalk Marl in the UK Channel Tunnels Using Q

2019 ◽  
pp. 477-502
Author(s):  
Nick Barton ◽  
Colin Warren
Keyword(s):  
Rock Mass ◽  
Rock Mass Classification ◽  
Mass Classification ◽  
The Uk

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

scholarly journals Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions Ahead of a TBM Using Data-Driven Approach

2021 ◽  
Vol 11 (3) ◽  
pp. 1060
Author(s):  
Sylvanus Sebbeh-Newton ◽  
Prosper E.A. Ayawah ◽  
Jessica W.A. Azure ◽  
Azupuri G.A. Kaba ◽  
Fauziah Ahmad ◽  
...  
Keyword(s):  
Rock Mass ◽  
Tunnel Boring Machine ◽  
Rock Mass Classification ◽  
Data Driven ◽  
Training Dataset ◽  
Ground Conditions ◽  
Data Driven Approach ◽  
Mass Classification ◽  
Using Data

Pre-tunneling exploration for rock mass classification is a common practice in tunneling projects. This study proposes a data-driven approach that allows for rock mass classification. Two machine learning (ML) classification models, namely random forest (RF) and extremely randomized tree (ERT), are employed to classify the rock mass conditions encountered in the Pahang-Selangor Raw Water Tunnel in Malaysia using tunnel boring machine (TBM) operating parameters. Due to imbalance of rock classes distribution, an oversampling technique was used to obtain a balanced training dataset for unbiased learning of the ML models. A five-fold cross-validation approach was used to tune the model hyperparameters and validation-set approach was used for the model evaluation. ERT achieved an overall accuracy of 95%, while RF achieved 94% accuracy, in rightly classifying rock mass conditions. The result shows that the proposed approach has the potential to identify and correctly classify ground conditions of a TBM, which allows for early problem detection and on-the-fly support system selection based on the identified ground condition. This study, which is part of an ongoing effort towards developing reliable models that could be incorporated into TBMs, shows the potential of data-driven approaches for on-the-fly classification of ground conditions ahead of a TBM and could allow for the early detection of potential construction problems.

Modified rock mass classification system by continuous rating

2003 ◽  
Vol 67 (3-4) ◽  
pp. 269-280 ◽  
Author(s):  
Zekai Şen ◽  
Bahaaeldin H. Sadagah
Keyword(s):  
Rock Mass ◽  
Classification System ◽  
Rock Mass Classification ◽  
Mass Classification
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