Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data

•An ensemble model AdaBoost-CART is proposed to predict rock mass classification.•SMOTE is utilized to address the imbalance ratio of rock mass classifications.•The AdaBoost-CART model performs better than conventional machine learning methods.•The variable importance of the model is analyzed. The r...

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Bibliographic Details
Published in:Tunnelling and underground space technology Vol. 106; p. 103595
Main Authors: Liu, Quansheng, Wang, Xinyu, Huang, Xing, Yin, Xin
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.12.2020
Elsevier BV
Subjects:
AdaBoost-CART model
Algorithms
Artificial neural networks
Boring machines
Classification
Hydroelectric power
Integrated algorithm
Machine learning
Prediction models
Real time
Regression analysis
Rock mass classification
Rock mass rating
Rocks
Sampling methods
SMOTE
Statistical analysis
Statistical methods
Studies
TBM
Tunnel construction
Tunnels
SMOTE
Integrated algorithm
TBM
Rock mass classification
AdaBoost-CART model
ISSN:0886-7798, 1878-4364
Online Access:Get full text
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Summary:•An ensemble model AdaBoost-CART is proposed to predict rock mass classification.•SMOTE is utilized to address the imbalance ratio of rock mass classifications.•The AdaBoost-CART model performs better than conventional machine learning methods.•The variable importance of the model is analyzed. The real-time acquisition of surrounding rock information is important for the efficient tunneling and hazard prevention in tunnel boring machines (TBMs). This study presents an ensemble learning model based on classification and regression tree (CART) and AdaBoost algorithm to predict the classification of surrounding rock mass. Statistical indicators (i.e., mean value and standard deviation) of TBM operational parameters were calculated and used as input variables, and the rock mass classification obtained by the hydropower classification (HC) method was used as output variable. To develop the model, a database was established, consisting of 3166 samples collected from the Songhua River Water Conveyance Tunnel. The synthetic minority over-sampling technique (SMOTE) was utilized to address the imbalance ratio of rock mass classifications in the database. The results of the testing set showed that the accuracy and F1-measure of AdaBoost-CART were 0.865 and 0.770, respectively, which are better than the results of the standard CART (0.753 and 0.629, respectively). The application of SMOTE improves the recall of minority classes. Compared with artificial neural networks, k-nearest neighbor, and support vector classifier, the developed AdaBoost-CART model achieves better performance. The variable importance was analyzed to distinguish key features; the results showed that rock mass boreability may not be a major consideration of the HC method. The presented model can provide significant guidance for the real-time acquisition of surrounding rock information during TBM tunneling.
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ISSN:0886-7798
1878-4364
DOI:10.1016/j.tust.2020.103595