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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

Clicked: 817

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jie LI

https://orcid.org/0000-0002-5263-1651

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

http://doi.org/10.1631/jzus.A2200516


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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Abstract: 
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.

一种数据驱动隧道掘进机推力的建模与预测方法

作者:王林涛1,朱沣樟1,李杰1.2,孙伟1
机构:1大连理工大学,机械工程学院,中国大连,116024;2北方自动控制技术研究所,中国太原,030006
目的:为克服隧道掘进机(TBM)本身因素(掘进速度、盾构机岩压和刀盘水压力等)以及随机地质条件等复杂工况对于TBM推力建模的挑战,本文期望通过将现场质量记录数据与代理模型技术相结合建立高精度TBM推力预测模型,以提高TBM推力预测的精度并有效改善开挖计划。
创新点:1.现场记录数据与代理模型技术相结合构建高精度预测模型;2.使用莫里斯法进行因素筛选从而提高建模精度;3.通过相似工况建模并适当增加建模样本有效提高了TBM推力预测精度。
方法:1.通过对现场数据进行筛选并对TBM推力的来源进行分析,得出可能影响推力的21个因素;2.基于上述影响因素构建4种代理模型并比较精度;3.使用莫里斯法进行主因素筛选,并选用精度最高的克里金模型进行TBM推力建模和预测;4.构建三种不同工况条件下的推力预测模型并比较预测误差。
结论:1.现场记录数据与代理模型技术相结合构建的高精度预测模型为TBM在复杂工况下推力的建模和预测提供了一种可行的方式。2.经过莫里斯法进行主因素筛选后,代理模型的精度得到有效提高。3.采用相似工况建模以及合理增加建模成本可以有效提高预测的精度;基于数据驱动的TBM推力预测模型可作为掘进过程中控制推力的重要依据。

关键词:隧道掘进机;推力预测;代理模型;莫里斯法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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