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Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction
Jie Yang
,
Saffet Yagiz
, Ying Jing Liu
, Farid Laouafa
School of Mining and Geosciences
Nazarbayev University
Zhongtian Construction Group Co. Ltd.
Hong Kong Polytechnic University
Institut national de l'environnement industriel et des risques
Research output
:
Contribution to journal
›
Article
›
peer-review
28
Citations (Scopus)
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INIS
performance
100%
prediction
100%
evaluation
100%
algorithms
100%
randomness
100%
machine learning
100%
polynomials
100%
forests
100%
output
60%
comparative evaluations
40%
accuracy
40%
datasets
40%
interactions
20%
engineers
20%
distance
20%
rocks
20%
multivariate analysis
20%
brittleness
20%
compression strength
20%
Engineering
Performance Prediction
100%
Tunnel Boring Machine
100%
Machine Learning Algorithm
100%
Random Forest
100%
Input Parameter
60%
Output Parameter
40%
Accurate Prediction
20%
Explicit Expression
20%
Penetration Rate
20%
Uniaxial Compressive Strength
20%
Intact Rock
20%
Brittleness Index
20%
Keyphrases
Novel Prediction
20%