Development of a PLSR-BRT Model for Predicting the Performance of Tunnel Boring Machines
Publication: International Journal of Geomechanics
Volume 23, Issue 3
Abstract
Scientific prediction of the penetration rate (PR) of tunnel boring machines (TBMs) is of great significance to the selection of tunnel construction methods, the prediction of construction progress, and the evaluation of benefits. In view of the high nonlinearity, fuzziness, and complexity of the TBM excavation process, this paper attempts to present a new algorithm that integrates partial least squares regression (PLSR) with boosted regression trees (BRT) for predicting the PR of TBMs. For this purpose, 150 datasets were obtained from the Lanzhou water conveyance tunnel project in China. Six impact factors were selected as the input layers of the proposed model by single-factor correlation analysis. To develop the PLSR-BRT model, two principal components of the influencing parameters were extracted via the PLSR method, and the BRT algorithm was employed to establish the predictive model. In addition, other models such as PLSR back propagation neural network (BPNN), BRT, PLSR, support vector regression (SVR), and artificial neural network (ANN) were adapted for use in this problem to verify the predictive accuracy of the proposed model. The results demonstrated that the PLSR-BRT model achieved the best predictive performance compared with the other models, with coefficient of determination value (R2) and root mean square error (RMSE) of 0.96 and 1.78, respectively, for the testing set. The PLSR-BRT model can maximally avoid both overfitting and inadequate fitting. In view of engineering applications, the complexity and redundancy of the field dataset can be solved via reducing the dimensionality of input parameters, and the training samples could be expanded by a boosted method so that the discontinuously acquired geological parameters are unable to restrain the model performance. This method serves as an effective approach for TBM PR prediction and has excellent potential for a variety of scientific and engineering applications.
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Acknowledgments
This research was financially supported by the National Natural Science Foundation of China (Nos. 41972270, 51909241 and 52279114) and the Research Fund of Key Laboratory of Water Management and Water Security for Yellow River Basin, Ministry of Water Resources (under construction) (2022-SYSJJ-06). The author contributions are as follows: Changbin Yan, Data curation, Writing—original draft; Gaoliu Li, Writing—review, Software; Hejian Wang, Conceptualization, Methodology, Software; Shuqian Duan, Writing—review and editing, Supervision.
Notation
The following symbols are used in this paper:
- E0
- Young’s modulus, GPa;
- f
- friction force, KN;
- QTBM
- modified Q system;
- R2
- determination coefficient;
- U
- utilization, %;
- α
- angle between the tunnel axis and the plane of weakness; and
- μ
- Poisson’s ratio.
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Information & Authors
Information
Published In
International Journal of Geomechanics
Volume 23 • Issue 3 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 14, 2022
Accepted: Sep 18, 2022
Published online: Dec 27, 2022
Published in print: Mar 1, 2023
Discussion open until: May 27, 2023
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Boring
- Buses
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction methods
- Drilling
- Engineering fundamentals
- Equipment and machinery
- Geotechnical engineering
- Highway transportation
- Infrastructure
- Neural networks
- Public transportation
- Rapid transit systems
- Regression analysis
- Statistical analysis (by type)
- Transportation engineering
- Tunnels
- Vehicles
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