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On-line Access: 2023-06-20

Received: 2022-10-28

Revision Accepted: 2023-02-26

Crosschecked: 2023-09-20

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Jie LI


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Journal of Zhejiang University SCIENCE A 2023 Vol.24 No.9 P.801-816


A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine

Author(s):  Lintao WANG, Fengzhang ZHU, Jie LI, Wei SUN

Affiliation(s):  School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China; more

Corresponding email(s):   leejiedlut@163.com

Key Words:  Tunnel boring machine (TBM), Thrust prediction, Surrogate model, Morris method

Lintao WANG, Fengzhang ZHU, Jie LI, Wei SUN. A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine[J]. Journal of Zhejiang University Science A, 2023, 24(9): 801-816.

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author="Lintao WANG, Fengzhang ZHU, Jie LI, Wei SUN",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine
%A Lintao WANG
%A Fengzhang ZHU
%A Jie LI
%A Wei SUN
%J Journal of Zhejiang University SCIENCE A
%V 24
%N 9
%P 801-816
%@ 1673-565X
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200516

T1 - A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine
A1 - Lintao WANG
A1 - Fengzhang ZHU
A1 - Jie LI
A1 - Wei SUN
J0 - Journal of Zhejiang University Science A
VL - 24
IS - 9
SP - 801
EP - 816
%@ 1673-565X
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2200516

thrust prediction of a tunnel boring machine (TBM) is crucial for the life span of disc cutters, cost forecasting, and its design optimization. Many factors affect the thrust of a TBM. The rock pressure on the shield, advance speed, and cutter water pressure will all have a certain impact. In addition, geological conditions and other random factors will also influence the thrust and greatly increase the difficulty of modeling it, seriously affecting the efficiency of tunnel excavation. To overcome these challenges, this paper establishes a thrust prediction model for the TBM based on the combination of on-site quality record data and surrogate model technology. Firstly, the thrust composition and influencing factors are analyzed and the thrust is modeled using a surrogate model based on field data. After main factor screening based on the morris method, the accuracy of the surrogate model is greatly improved. The Kriging model with the highest accuracy is selected to model the thrust and predict the thrust of the unexcavated section. The results show that the thrust model has better thrust prediction by selecting similar conditions for modeling and reasonably increasing modeling samples. The thrust prediction method of TBM based on the combination of field data and surrogate model can accurately predict the dynamic thrust of the load and can also accurately estimate its statistical characteristics and effectively improve the excavation plan.




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