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.
摘要
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。
References
Barton NR, 1999. TBM performance estimation in rock using Q(TBM). Tunnels & Tunnelling International, 31(9):30–34.
Breiman L, 2001. Random forests. Machine Learning, 45(1): 5–32. https://doi.org/10.1023/A:1010933404324
Buhmann MD, 2000. Radial basis functions. Acta Numerica, 9:1–38. https://doi.org/10.1017/S0962492900000015
Copur H, Aydin H, Bilgin N, et al., 2014. Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model. Tunnelling and Underground Space Technology, 42:1–14. https://doi.org/10.1016/j.tust.2014.01.006
Gutmann HM, 2001. A radial basis function method for global optimization. Journal of Global Optimization, 19:201–227. https://doi.org/10.1023/A:1011255519438
Hassanpour J, Rostami J, Khamehchiyan M, et al., 2010. TBM performance analysis in pyroclastic rocks: a case history of Karaj water conveyance tunnel. Rock Mechanics and Rock Engineering, 43(4):427–445. https://doi.org/10.1007/s00603-009-0060-2
Huo JZ, Zhang HD, Xu ZH, et al., 2022. Coupling dynamic characteristics of tunnel boring machine cutterhead system with multi-source uncertainties. Engineering Failure Analysis, 137:106180. https://doi.org/10.1016/j.engfailanal.2022.106180
Iliadis L, Jayne C, Tefas A, et al., 2022. Engineering Applications of Neural Networks. Springer, Cham, Germany. https://doi.org/10.1007/978-3-031-08223-8
Jean WH, Sutikno P, Fan SZ, et al., 2022. Comparison of deep learning algorithms in predicting expert assessments of pain scores during surgical operations using analgesia nociception index. Sensors, 22(15):5496. https://doi.org/10.3390/s22155496
Krause H, 1976. Geologische erfahrungen beim einsatz von tunnelvortriebsmaschinen in Baden-Württemberg. In: für Geomechanik ÖG (Ed.), Neue Erkenntnisse im Hohlraumbau—Fundierungen im Fels/Latest Findings in the Construction of Underground Excavations— Rock Foundations. Springer, Vienna, Austria, p.49–60 (in German). https://doi.org/10.1007/978-3-7091-8452-3_3
Li WJ, Zeng QL, Yin L, et al., 2011. Analysis on force model of wedge-shaped milling cutters and influence laws. Mining & Processing Equipment, 39(10): 112–117 (in Chinese). https://doi.org/10.16816/j.cnki.ksjx.2011.10.029
Lin SS, Zhang N, Zhou AN, et al., 2022. Time-series prediction of shield movement performance during tunneling based on hybrid model. Tunnelling and Underground Space Technology, 119:104245. https://doi.org/10.1016/j.tust.2021.104245
Lv F, Yu J, Zhang J, et al., 2022. A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 23(12): 1027–1046. https://doi.org/10.1631/jzus.A2200297
Masi F, Stefanou I, Vannucci P, et al., 2021. Thermodynamics-based artificial neural networks for constitutive modeling. Journal of the Mechanics and Physics of Solids, 147: 104277. https://doi.org/10.1016/j.jmps.2020.104277
Meng XH, Babaee H, Karniadakis GE, 2021. Multi-fidelity Bayesian neural networks: algorithms and applications. Journal of Computational Physics, 438:110361. https://doi.org/10.1016/j.jcp.2021.110361
Morris MD, 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2): 161–174. https://doi.org/10.2307/1269043
Mullur AA, Messac A, 2006. Metamodeling using extended radial basis functions: a comparative approach. Engineering with Computers, 21(3):203–217. https://doi.org/10.1007/s00366-005-0005-7
Myers RH, Montgomery DC, Anderson-Cook CM, 2016. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 4th Edition. John Wiley & Sons, Hoboken, USA.
Raissi M, Perdikaris P, Karniadakis GE, 2019. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707. https://doi.org/10.1016/j.jcp.2018.10.045
Regis RG, Shoemaker CA, 2005. Constrained global optimization of expensive black box functions using radial basis functions. Journal of Global Optimization, 31(1): 153–171. https://doi.org/10.1007/s10898-004-0570-0
Rosso MM, Marasco G, Aiello S, et al., 2023. Convolutional networks and transformers for intelligent road tunnel investigations. Computers & Structures, 275:106918. https://doi.org/10.1016/j.compstruc.2022.106918
Sacks J, Welch WJ, Mitchell TJ, et al., 1989. Design and analysis of computer experiments. Statistical Science, 4(4): 409–423. https://doi.org/10.1214/ss/1177012413
Saltelli A, Tarantola S, Campolongo F, et al., 2004. Sensitivity Analysis in Practice: a Guide to Assessing Scientific Models. Wiley, Hoboken, USA.
Shi H, Yang HY, Gong GF, et al., 2011. Determination of the cutterhead torque for EPB shield tunneling machine. Automation in Construction, 20(8):1087–1095. https://doi.org/10.1016/j.autcon.2011.04.010
Sun W, Shi ML, Zhang C, et al., 2018. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Automation in Construction, 92:23–34. https://doi.org/10.1016/j.autcon.2018.03.030
Sun W, Peng X, Dou J, et al., 2020. Surrogate-based weight reduction optimization of forearm of bucket-wheel stacker reclaimer. Structural and Multidisciplinary Optimization, 61(3):1287–1301. https://doi.org/10.1007/s00158-019-02415-3
Tao Z, Tan XD, Han T, et al., 2010. Reconstruction of normal speech from whispered speech based on RBF neural network. Proceedings of the 3rd International Symposium on Intelligent Information Technology and Security Informatics, p.374–377. https://doi.org/10.1109/IITSI.2010.118
Williamson RC, Smola AJ, Scholkopf B, 2001. Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Transactions on Information Theory, 47(6): 2516–2532. https://doi.org/10.1109/18.945262
Yagiz S, 2017. New equations for predicting the field penetration index of tunnel boring machines in fractured rock mass. Arabian Journal of Geosciences, 10(2):33. https://doi.org/10.1007/s12517-016-2811-1
Yang L, Meng XH, Karniadakis GE, 2021. B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. Journal of Computational Physics, 425:109913. https://doi.org/10.1016/j.jcp.2020.109913
Yuan XF, Wang YN, Sun W, et al., 2010. RBF networks-based adaptive inverse model control system for electronic throttle. IEEE Transactions on Control Systems Technology, 18(3):750–756. https://doi.org/10.1109/TCST.2009.2026397
Zhang P, Yin ZY, Jin YF, 2022a. Machine learning-based modelling of soil properties for geotechnical design: review, tool development and comparison. Archives of Computational Methods in Engineering, 29(2):1229–1245. https://doi.org/10.1007/s11831-021-09615-5
Zhang P, Yin ZY, Jin YF, et al., 2022b. Physics-informed multifidelity residual neural networks for hydromechanical modeling of granular soils and foundation considering internal erosion. Journal of Engineering Mechanics, 148(4): 04022015. https://doi.org/10.1061/(ASCE)EM.1943-7889.0002094
Zhang Q, Huang T, Huang GY, et al., 2013. Theoretical model for loads prediction on shield tunneling machine with consideration of soil-rock interbedded ground. Science China Technological Sciences, 56(9):2259–2267. https://doi.org/10.1007/s11431-013-5302-6
Zhang Q, Qu CY, Cai ZX, et al., 2014. Modeling of the thrust and torque acting on shield machines during tunneling. Automation in Construction, 40:60–67. https://doi.org/10.1016/j.autcon.2013.12.008
Zhang ZH, Meng L, Sun F, 2014. Wear analysis of disc cutters of full face rock tunnel boring machine. Chinese Journal of Mechanical Engineering, 27(6): 1294–1300. https://doi.org/10.3901/CJME.2014.0905.145
Zheng YL, Zhang QB, Zhao J, 2016. Challenges and opportunities of using tunnel boring machines in mining. Tunnelling and Underground Space Technology, 57:287–299. https://doi.org/10.1016/j.tust.2016.01.023
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 5217052098) and the National Key Research and Development Program of China (No. 2020YFB2007203).
Author information
Authors and Affiliations
Contributions
Lintao WANG conducted the modeling and theoretical derivation of the approximate model and trained the sample of the approximate model. Jie LI analyzed the thrust composition and influencing factors of TBM and modeled the thrust of TBM by using agent model technology based on field data. Fengzhang ZHU improved the accuracy of the surrogate model greatly after the principal factor screening based on the Morris method, and wrote the first draft of the paper. Wei SUN revised and edited the final version.
Corresponding author
Ethics declarations
Lintao WANG, Fengzhang ZHU, Jie LI, and Wei SUN declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Wang, L., Zhu, F., Li, J. et al. A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine. J. Zhejiang Univ. Sci. A 24, 801–816 (2023). https://doi.org/10.1631/jzus.A2200516
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A2200516