CLC number:
On-line Access: 2024-05-16
Received: 2024-01-05
Revision Accepted: 2024-04-19
Crosschecked: 0000-00-00
Cited: 0
Clicked: 26
Junxing ZHAO, Xiaobin DING. Predicting tunnel boring machine performance with the informer model: a case study of the Guangzhou Metro Line project[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2400012
@article{title="Predicting tunnel boring machine performance with the informer model: a case study of the Guangzhou Metro Line project",
author="Junxing ZHAO, Xiaobin DING",
journal="Journal of Zhejiang University Science A",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.A2400012"
}
%0 Journal Article
%T Predicting tunnel boring machine performance with the informer model: a case study of the Guangzhou Metro Line project
%A Junxing ZHAO
%A Xiaobin DING
%J Journal of Zhejiang University SCIENCE A
%P
%@ 1673-565X
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/jzus.A2400012"
TY - JOUR
T1 - Predicting tunnel boring machine performance with the informer model: a case study of the Guangzhou Metro Line project
A1 - Junxing ZHAO
A1 - Xiaobin DING
J0 - Journal of Zhejiang University Science A
SP -
EP -
%@ 1673-565X
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/jzus.A2400012"
Abstract: Accurately forecasting the operational performance of a tunnel boring machine (TBM) in advance is useful for making timely adjustments to boring parameters, thereby enhancing overall boring efficiency. In this study we used the Informer model to predict a critical performance parameter of the TBM, namely thrust. Leveraging data from the Guangzhou Metro Line 22 project on the big data platform in China, the model's performance was validated, while data from Line 18 was used to assess its generalization capability. Results revealed that the Informer model surpasses Random Forest, Extreme Gradient Boosting, Support Vector Regression, k-Nearest Neighbors, Back Propagation, and Long Short-Term Memory models in both prediction accuracy and generalization performance. In addition, the optimal input lengths for maximizing accuracy in the single time-step output model are within the range of 8-24, while for the multiple time-step output model, the optimal input length is 8. Furthermore, the last predicted value in the case of multiple time-step outputs showed the highest accuracy. It was also found that relaxation of the Pearson's analysis method metrics to 0.95 improved the performance of the model. Finally, the prediction results were most affected by earth pressure, rotation speed, torque, boring speed, and the surrounding rock grade. The model can provide useful guidance for constructors when adjusting TBM operation parameters.
Open peer comments: Debate/Discuss/Question/Opinion
<1>