Intelligent Attitude Control Method for Shield Tunneling Machines Considering a Rectifying Mechanism: A Case Study of the Chengdu Subway
Publication: International Journal of Geomechanics
Volume 24, Issue 8
Abstract
During tunnel construction with earth pressure balance (EPB) shield machine, the machine operators determine the attitude correction parameters depending only on their own experiences. Inappropriate parameter setting may lead to more and more deviation of equipment attitude, delay of construction period, or even ground collapse. An attitude correction method for an EPB shield machine is proposed in this study to assist new machine operators in determining suitable operating parameters in advance considering the previous correction experience. The proposed method first reconstructs the tunneling parameters and attitude parameters according to experienced drivers. Then, using these parameters, an association model based on multiple machine algorithms is established to refine the important association rules of EPB shield machine attitude. Finally, the optimal range of deviation correction parameters corresponding to different attitude deviation is generated. This assisted attitude control method was examined using the data from the Chengdu Subway project in China. Essentially, this study can be helpful for the equipment attitude correction and the determination of correction parameters, paving the way for ideal track driving in harsh tunneling environments.
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Data Availability Statement
All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was supported by the Programme of Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station (No. ZDSYS20200923105200001), the National Natural Science Foundation of China (Nos. 52079150, 52179121), and the Core Research Project of Power Construction Corporation of China (No. DJ-HXGG-2021-01).
References
Agrawal, R., and R. Srikant. 1994. “Fast algorithms for mining association rules.” In Vol. 1215 of Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 487–499. Cambridge, MA: Morgan Kaufmann Publishers Inc.
Al-Maolegi, M., and B. Arkok. 2014. “An improved Apriori algorithm for association rules.” Preprint, submitted March 16, 2014. https://arxiv.org/abs/1403.3948.
Cai, J., J. Luo, S. Wang, and S. Yang. 2018. “Feature selection in machine learning: A new perspective.” Neurocomputing 300: 70–79. https://doi.org/10.1016/j.neucom.2017.11.077.
Chen, R., P. Zhang, H. Wu, Z. Wang, and Z. Zhong. 2019. “Prediction of shield tunneling-induced ground settlement using machine learning techniques.” Front. Struct. Civ. Eng. 13 (6): 1363–1378. https://doi.org/10.1007/s11709-019-0561-3.
Fu, X., M. Wu, S. Ponnarasu, and L. Zhang. 2023. “A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns.” Expert Syst. Appl. 212: 118721. https://doi.org/10.1016/j.eswa.2022.118721.
Gao, D. W., Y. S. Zhu, J. F. Zhang, Y. K. He, K. Yan, and B. R. Yan. 2021. “A novel MP-LSTM method for ship trajectory prediction based on AIS data.” Ocean Eng. 228: 108956. https://doi.org/10.1016/j.oceaneng.2021.108956.
Guo, D., J. Li, S. H. Jiang, X. Li, and Z. Chen. 2022. “Intelligent assistant driving method for tunnel boring machine based on big data.” Acta Geotech. 17 (4): 1019–1030. https://doi.org/10.1007/s11440-021-01327-1.
Huang, H., J. Chang, D. Zhang, J. Zhang, H. Wu, and G. Li. 2022. “Machine learning-based automatic control of tunneling posture of shield machine.” J. Rock Mech. Geotech. Eng. 14 (4): 1153–1164. https://doi.org/10.1016/j.jrmge.2022.06.001.
Khetwal, A., J. Rostami, and P. P. Nelson. 2022. “Understanding the effect of geology-related delays on performance of hard rock TBMs.” Acta Geotech. 17 (3): 919–929. https://doi.org/10.1007/s11440-021-01243-4.
Kim, D., K. Kwon, K. Pham, J. Y. Oh, and H. Choi. 2022. “Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization.” Autom. Constr. 140: 104331. https://doi.org/10.1016/j.autcon.2022.104331.
Kong, X., X. Ling, L. Tang, W. Tang, and Y. Zhang. 2022. “Random forest-based predictors for driving forces of earth pressure balance (EPB) shield tunnel boring machine (TBM).” Tunnelling Underground Space Technol. 122: 104373. https://doi.org/10.1016/j.tust.2022.104373.
Koopialipoor, M., A. Fahimifar, E. N. Ghaleini, M. Momenzadeh, and D. J. Armaghani. 2020. “Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance.” Eng. Comput.-Germany 36 (1): 345–357. https://doi.org/10.1007/s00366-019-00701-8.
Kuwahara, H., M. Harada, Y. Seno, and M. Takeuchi. 1988. “Application of fuzzy reasoning to the control of shield tunnelling.” Doboku Gakkai Ronbunshu 391: 169–178. https://doi.org/10.2208/jscej.1988.391_169.
Leng, S., J. R. Lin, Z. Z. Hu, and X. Shen. 2020. “A hybrid data mining method for tunnel engineering based on real-time monitoring data from tunnel boring machines.” IEEE Access 8: 90430–90449. https://doi.org/10.1109/ACCESS.2020.2994115.
Li, J., P. Li, D. Guo, X. Li, and Z. Chen. 2021a. “Advanced prediction of tunnel boring machine performance based on big data.” Geosci. Front. 12 (1): 331–338. https://doi.org/10.1016/j.gsf.2020.02.011.
Li, J., W. D. Wang, Z. Han, and G. Chen. 2021b. “Analysis of secondary-factor combinations of landslides using improved association rule algorithms: A case study of Kitakyushu in Japan.” Geomatics Nat. Hazards Risk 12 (1): 1885–1904. https://doi.org/10.1080/19475705.2021.1947904.
Li, Y., and H. Wu. 2012. “A clustering method based on K-means algorithm.” Physics Procedia 25: 1104–1109. https://doi.org/10.1016/j.phpro.2012.03.206.
Li, Z. Z. 2013. “The study of shield tunneling attitude control technology in soft soil layer.” [In Chinese.] Master Dissertation, School of Civil Engineering, Beijing Jiaotong Univ.
Likas, A., N. Vlassis, and J. J. Verbeek. 2003. “The global k-means clustering algorithm.” Pattern Recognit. 36 (2): 451–461. https://doi.org/10.1016/S0031-3203(02)00060-2.
Liu, C., H. S. Guan, and Y,H Xie. 2019a. “Study on driving posture and deviation correction curve of the shield machine.” Mod. Tunnelling Technol. 56 (04): 105–112+126.
Liu, T., G. F. Gong, H. Y. Yang, Y. X. Chen, and Y. Zhu. 2019b. “Trajectory control of tunnel boring machine based on adaptive rectification trajectory planning and multi-cylinders coordinated control.” Int. J. Precis. Eng. Manuf. 20 (10): 1721–1733. https://doi.org/10.1007/s12541-019-00073-5.
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, and É Duchesnay. 2011. “Scikit-learn: Machine learning in python.” J. Mach. Learn. Res. 12: 2825–2830.
Raschka, S. 2018. “MLxtend: Providing machine learning and data science utilities and extensions to python’s scientific computing stack.” J. Open Source Software 3 (24): 638. https://doi.org/10.21105/joss.00638.
Sang, Y. 2012. “Research on discretization methods for continuous data.” [In Chinese.] Master Dissertation, School of Computer Science and Technology, Dalian University of Technology.
Shen, S. L., K. Elbaz, W. M. Shaban, and A. Zhou. 2022. “Real-time prediction of shield moving trajectory during tunnelling.” Acta Geotech. 17 (4): 1533–1549. https://doi.org/10.1007/s11440-022-01461-4.
Sugimoto, M., A. Sramoon, S. Konishi, and Y. Sato. 2007. “Simulation of shield tunneling behavior along a curved alignment in a multilayered ground.” J. Geotech. Geoenviron. Eng. 133 (6): 684–694. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:6(684).
Wang, L., X. Yang, G. Gong, and J. Du. 2018. “Pose and trajectory control of shield tunneling machine in complicated stratum.” Autom. Constr. 93: 192–199. https://doi.org/10.1016/j.autcon.2018.05.020.
Wang, P., X. Kong, Z. Guo, and L. Hu. 2019. “Prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling.” IEEE Access 7: 163487–163501. https://doi.org/10.1109/ACCESS.2019.2952649.
Wu, H. H., J. Q. Chang, and G. Li. 2021. “Prediction of driving posture and optimization of construction parameters for shield based on support vector machine.” Tunnel Constr. 41 (S1): 11–18.
Xia, H. Y., H. J. Yin, and J. H. Xu. 2021. “Multi-construction parameter shield construction attitude prediction based on machine learning.” Bull. Surv. Mapp. (1): 5.
Xiao, H., Z. Chen, R. Cao, Y. Cao, L. Zhao, and Y. Zhao. 2022. “Prediction of shield machine posture using the GRU algorithm with adaptive boosting: A case study of Chengdu subway project.” Transp. Geotech. 37: 100837. https://doi.org/10.1016/j.trgeo.2022.100837.
Xiao, H., B. Xing, Y. Wang, P. Yu, L. Liu, and R. Cao. 2021. “Prediction of shield machine attitude based on various artificial intelligence technologies.” Appl. Sci.-Basel 11 (21): 10264. https://doi.org/10.3390/app112110264.
Xie, H., X. Duan, H. Yang, and Z. Liu. 2012. “Automatic trajectory tracking control of shield tunneling machine under complex stratum working condition.” Tunnelling Underground Space Technol. 32: 87–97. https://doi.org/10.1016/j.tust.2012.06.002.
Xie, H., Z. Liu, and H. Yang. 2014. “Advancing control for shield tunneling machine by backstepping design with LuGre friction model.” Math. Probl. Eng. 1–10.
Yan, T., S. L. Shen, and A. Zhou. 2023. “Identification of geological characteristics from construction parameters during shield tunnelling.” Acta Geotech. 18 (1): 535–551. https://doi.org/10.1007/s11440-022-01590-w.
Yang, H., K. Song, and J. Zhou. 2022. “Automated recognition model of geomechanical information based on operational data of tunneling boring machines.” Rock Mech. Rock Eng. 55: 1–18.
Yin, J., C. Ning, and T. Tang. 2022. “Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction.” Inf. Sci. 600: 377–400. https://doi.org/10.1016/j.ins.2022.04.004.
Zhang, N., N. Zhang, Q. Zheng, and Y. S. Xu. 2022. “Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network.” Acta Geotech. 17 (4): 1167–1182. https://doi.org/10.1007/s11440-021-01319-1.
Zhang, P., R. P. Chen, and H. N. Wu. 2019. “Real-time analysis and regulation of EPB shield steering using random forest.” Autom. Constr. 106: 102860. https://doi.org/10.1016/j.autcon.2019.102860.
Zhang, Z., and L. Ma. 2018. “Attitude correction system and cooperative control of tunnel boring machine.” Int. J. Pattern Recognit Artif. Intell. 32 (11): 1859018. https://doi.org/10.1142/S0218001418590188.
Zhou, C., H. Xu, L. Ding, L. Wei, and Y. Zhou. 2019. “Dynamic prediction for attitude and position in shield tunneling: A deep learning method.” Autom. Constr. 105: 102840. https://doi.org/10.1016/j.autcon.2019.102840.
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© 2024 American Society of Civil Engineers.
History
Received: Mar 23, 2023
Accepted: Jan 13, 2024
Published online: May 29, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 29, 2024
ASCE Technical Topics:
- Algorithms
- Case studies
- Construction engineering
- Construction equipment
- Construction methods
- Engineering fundamentals
- Equipment and machinery
- Geotechnical engineering
- Infrastructure
- Mathematics
- Methodology (by type)
- Parameters (statistics)
- Rail transportation
- Research methods (by type)
- Statistics
- Subways
- Transportation engineering
- Tunneling
- Tunnels
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