Thrust-vectoring automatic shield tunneling technology: Method, verification and application

Abstract

Recognizing the formidable challenge of achieving millimeter-level precision in controlling shield machine attitudes amidst thrust forces exceeding thousands of tons on a global scale, a thrust-vectoring automatic shield tunneling technology was introduced to effectively mitigate potential inaccuracies stemming from human intervention. Initially, a load-thrust “dual-vector” motion control mechanism was adopted, grounded in defining the shield thrust vector and establishing the interactive correlation between shield attitude deviation points and thrust action points in both horizontal and vertical orientations through comprehensive data assessments. Subsequently, a parallel proportional-integral-derivative control law was devised for stability control of shield machines, delineating the functional link between alterations in shield attitudes and displacements of thrust action points, with initial validation conducted via full-scale model trials. A motion trajectory for correcting shield attitudes was devised, and a thrust vector control approach was formulated by amalgamating feedforward calculations with feedback adjustments. The application of this thrust-vectoring automatic tunneling technology in a large-diameter shield tunneling endeavor yielded the subsequent key findings: a consistent deviation of approximately 2.5% was upheld between target and actual thrust forces, with actual shield velocity managed within a -1 to +1 mm/min range from the target value. To ensure robust steering capability of the shield machine, target thrust moments in both horizontal and vertical directions marginally exceeded actual values, with satisfactory execution. The interplay between shield attitudes and thrust action points in both horizontal and vertical dimensions exhibited a characteristic akin to “sugar-coated haws on a stick”. Despite notable “kowtow” occurrences during segment assembly, statistical analysis indicated that deviations in shield attitude in horizontal and vertical planes were ultimately contained within -20 to +5 mm and -45 to -28 mm ranges, respectively, markedly surpassing average manual control standards.

Keywords

Shield machine / Automatic shield tunneling / Shield attitude / Thrust vector / Control law / Model test platform / Engineering application

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Yeting Zhu: Writing - original draft, Methodology, Formal analysis. Di Wu: Data curation. Zhihua Wang: Investigation. Zixin Zhang: Conceptualization, Supervision. Shuaifeng Wang: Visualization, Software. Xin Huang: Writing - review & editing, Validation, Supervision. Yuan Qin: Validation. Yanfei Zhu: Project administration, Funding acquisition. Fan Wang: Resources.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The research was supported by the State-owned Assets Supervision and Administration Commission of Shanghai, China (Grant No. 2022020).

References

[1] Afridi, S., Khan, T. A., Shah, S. I. A., Shams, T. A., Mohiuddin, K., & Kukulka, D. J. (2023). Techniques of fluidic thrust vectoring in jet engine nozzles: A review. Energies, 16(15), 5721.

[2] Cai, J. (2022). Research and application of shield automatic tunneling technology based on multilayer perceptron autoregressive model. Tunnel Construction, 42(10), 1797-1803 (in Chinese).

[3] Festa, D., Broere, W., & Bosch, J. W. (2015). Kinematic behaviour of a tunnel boring machine in soft soil: Theory and observations. Tunnelling and Underground Space Technology, 49, 208-217.

[4] Fu, X. L., Ponnarasu, S., Zhang, L. M., & Tiong, R. L. K. (2024). Online multi-objective optimization for real-time TBM attitude control with spatio-temporal deep learning model. Automation in Construction, 158, 105220.

[5] Fu, X. L., Wu, M. Z., Ponnarasu, S., & Zhang, L. M. (2023). A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns. Expert Systems with Applications, 212, 118721.

[6] He, B. N., Zhu, G. L., Han, L., & Zhang, D. L. (2020). Adaptive-neuro-fuzzy-based information fusion for the attitude prediction of TBMs. Sensors, 21(1), 61.

[7] Hu, M., Wu, B. J., Zhou, W. B., Wu, H. M., Li, G., Lu, J., Yu, G., & Qin, Y. (2022). Self-driving shield: Intelligent systems, methodologies, and practice. Automation in Construction, 139, 104326.

[8] Huai, R. G., Huang, S. Y., Zhong, X. C., Chen, X. Q., & Hu, Y. K. (2022). Development and engineering application of novel synchronous double component grouting behind shield segment. Tunnel Construction, 42(9), 1521-1528 (in Chinese).

[9] Invernizzi, D., & Lovera, M. (2018). Trajectory tracking control of thrust-vectoring UAVs. Automatica, 95, 180-186.

[10] Invernizzi, D., Lovera, M., & Zaccarian, L. (2020). Dynamic attitude planning for trajectory tracking in thrust-vectoring UAVs. IEEE Transactions on Automatic Control, 65(1), 453-460.

[11] Jia, G. P., Huo, J. Z., Yang, B. W., & Wu, Z. (2023). The real-time optimal attitude control of tunnel boring machine based on reinforcement learning. Applied Science, 13(18), 10026.

[12] Jian, P., Yang, C., You, Y. S., Jin, L. J., Zang, J. Q., & Xu, S. T. (2023). Research and application of automatic control technology for shield tunneling attitude. Tunnel Construction, 43(10), 1795-1800 (in Chinese).

[13] Kong, X. X., Ling, X. Z., Tang, L., Tang, W. C., & Zhang, Y. F. (2022). Random forest-based predictors for driving forces of earth pressure balance (EPB) shield tunnel boring machine (TBM). Tunnelling and Underground Space Technology, 122, 104373.

[14] Li, L., Hu, C. M., & Hou, Y. J. (2020). Prediction analysis of shield vertical attitude based on GRU. Journal of Physics: Conference Series, 1651(1), 012032.

[15] Li, X. F., & Gong, G. F. (2019). Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization. Automation in Construction, 107, 102928.

[16] Liu, T., Gong, G. F., Yang, H. Y., Chen, Y. X., & Zhu, Y. (2019). Trajectory control of tunnel boring machine based on adaptive rectification trajectory planning and multi-cylinders coordinated control. International Journal of Precision Engineering and Manufacturing, 20, 1721-1733.

[17] Qian, Q. H., Hu, X. Q., Li, S. C., Chen, J., & Zhu, W. B. (2024). Recent advances in key technologies of shield tunnel engineering in China. Tunnel Construction, 44(5), 897-926 (in Chinese).

[18] Ren, Y. Y., Sun, Z. C., & Chu, C. H. (2019). An attitude control strategy for shield thrusting. Tunnel Construction, 39(6), 1038-1044 (in Chinese).

[19] Soranzo, E., Guardiani, C., & Wu, W. (2023). Reinforcement learning for the face support pressure of tunnel boring machines. Geosciences, 13(3), 82.

[20] Sun, W., Shi, M. L., Zhang, C., Zhao, J. H., & Song, X. G. (2018). Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Automation in Construction, 92, 23-34.

[21] Tang, L., Kong, X. X., Ling, X. Z., Zhao, Y. Z., Tang, W. C., & Zhang, Y. F. (2022). Deviation correction strategy for the earth pressure balance shield based on shield-soil interactions. Frontiers of Mechanical Engineering, 17, 20.

[22] Tian, H. M., & Wang, Y. (2023). Data-driven and physics-informed Bayesian learning of spatiotemporally varying consolidation settlement from sparse site investigation and settlement monitoring data. Computers and Geotechnics, 157, 105328.

[23] Wang, K. Y., Wu, X. G., Zhang, L. M., & Song, X. Q. (2023a). Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach. Advanced Engineering Informatics, 55, 101854.

[24] Wang, L. T., Yang, X., Gong, G. F., & Du, J. N. (2018). Pose and trajectory control of shield tunneling machine in complicated stratum. Automation in Construction, 93, 192-199.

[25] Wang, L. T., Zhu, F. Z., Li, J., & Sun, W. (2023b). A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine. Journal of Zhejiang University-SCIENCE A, 24, 801-816.

[26] Wang, P., Kong, X. G., Guo, Z. K., & Hu, L. (2019). Prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling. IEEE Access, 7, 163487-163501.

[27] Wang, X. Y., Wang, X. Y., Ma, B., Li, Q., Wang, C. P., & Shi, Y. Q. (2023c). High-performance reversible data hiding based on ridge regression prediction algorithm. Signal Processing, 204, 108818.

[28] Wang, X. Y., Yuan, D. J., Jin, D. L., Jin, H., Yang, Y., & Wu, J. (2022). Determination of thrusts for different cylinder groups during shield tunneling. Tunnelling and Underground Space Technology, 127, 104579.

[29] Xie, P., Chen, K., Yin, Z. W., Zhu, Y. T., Luo, H. B., & Zhang, Q. B. (2024). A BIM-based multi-model framework for advancing TBM performance part 1: Real-time prediction of thrust force. Tunnelling and Underground Space Technology, 151, 105856.

[30] Xu, J., Bu, J. F., Qin, N., & Huang, D. Q. (2024). SCA-MADRL: Multiagent deep reinforcement learning framework based on state classification and assignment for intelligent shield attitude control. Expert Systems with Applications, 235, 121258.

[31] Yue, M., Sun, W., & Hu, P. (2012). Dynamic coordinated control of attitude correction for the shield tunneling based on load observer. Automation in Construction, 24, 24-29.

[32] Zeng, L., Shu, W. Q., Liu, Z., Zou, X. Y., Wang, S. S., Xia, J. Y., Xu, C., Xiong, D. D., & Yang, Z. (2022). Vision-based high-precision intelligent monitoring for shield tail clearance. Automation in Construction, 134, 104088.

[33] Zhang, N., Zhang, N., Zheng, Q., & Xu, Y. S. (2022a). Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network. Acta Geotechnica, 17, 1167-1182.

[34] Zhang, Q., Qu, C. Y., Cai, Z. X., Kang, Y. L., & Huang, T. (2014). Modeling of the thrust and torque acting on shield machines during tunneling. Automation in Construction, 40, 60-67.

[35] Zhang, W. J., Yang, Y., Zhang, C., Zhang, G. L., He, L. C., & Lu, J. R. (2023a). Dynamic autonomous deviation correction of super-large diameter shield tunnel rings based on multi-objective control techniques. China Journal of Highway and Transport, 36(11), 231-243 (in Chinese).

[36] Zhang, W. S., Liu, X. Y., Zhang, L. Y., & Wang, Y. D. (2023b). Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM. Engineering Research Express, 5(3), 035013.

[37] Zhang, Y. K., Gong, G. F., Yang, H. Y., Chen, Y. X., & Chen, G. L. (2022b). Towards autonomous and optimal excavation of shield machine a deep reinforcement learning-based approach. Journal of Zhejiang University-SCIENCE A, 23(6), 458-478.

[38] Zhen, M., Dong, X. Z., Liu, X. Y., & Tan, C. Q. (2024). Accelerated formulation of optimal control law for adaptive cycle engines: A novel design methodology. Aerospace Science and Technology, 148, 109076.

[39] Zheng, Z., Luo, K. D., Tan, X. Z., Jia, L. H., Xie, M. R., Xie, H. B., Jiang, L. J., Gong, G. F., Yang, H. Y., & Han, D. (2024). Autonomous steering control for tunnel boring machines. Automation in Construction, 159, 105259.

[40] Zhou, B., & Zhao, M. (2020). Numerical simulation of thruster-thruster interaction for ROV with vector layout propulsion system. Ocean Engineering, 210, 107542.

[41] Zhu, Y. T., Zhu, Y. F., Chen, E. J., Zhai, Y. X., Min, R., Tang, B., & Huang, X. (2023). Synchronous shield tunnelling technology combining advancement and segment fabrication: Principle, verification and application. Underground Space, 13, 23-47.