Intelligent attitude control of tunnel boring machine in synchronous operations of excavation and segment assembly

Abstract

This study introduces a shield attitude control method aimed at improving the accuracy and stability of tunnel boring machine (TBM) in synchronous excavation and segment assembly (S-TBM) along the design tunnel axis. The proposed hybrid deep learning model integrates bidirectional long short-term memory (BiLSTM) and Kolmogorov–Arnold network (KAN) cells to accurately predict S-TBM attitude. This model is further combined with the multi-objective grey wolf optimizer (MOGWO) method for determining the optimal proportional-integral-derivative (PID) parameters, thereby minimizing attitude deviations. The experimental results indicate that: (1) The S-TBM reduces the construction time per single lining ring to 39 min, which is 34 min faster than the conventional TBM. (2) The improved BiLSTM model can effectively estimate shield attitude, with an RMSE of 1.030, an MAE of 0.695, and an R² score of 0.998. (3) The proposed multi-objective optimization (MOO) framework is able to reliably control S-TBM attitude, achieving an overall improvement percentage of 25.66%. (4) Compared to other methods, the proposed approach enhances R² score by 0.03 and the overall improvement percentage by 5.72%. The novelty of this research lies in the development of an intelligent control framework for shield attitude under the synchronous excavation and segment assembly construction mode, which achieves reliable prediction and precise regulation of S-TBM attitude by synergistically coupling a nonlinear attitude response model based on an improved BiLSTM with an MOGWO-driven PID parameter optimization strategy.

Keywords

Synchronous excavation and segment assembly / Shield attitude control / Bidirectional long short-term memory network / Kolmogorov–Arnold network / Multi-objective grey wolf optimizer

References

[1] Abd Elaziz, M., Fares, I. A., & Aseeri, A. O. (2024). Ckan: Convolutional kolmogorov-arnold networks model for intrusion detection in iot environment. IEEE Access, 12, 134837-134851.

[2] Baykan, N. A., & Yılmaz, N. (2011). A mineral classification system with multiple artificial neural network using k-fold cross validation. Mathematical and Computational Applications, 16(1), 22-30.

[3] Chen, C., Wu, B., Jia, P., & Wang, Z . (2022). A novel hybrid deep neural network prediction model for shield tunneling machine thrust. IEEE Access, 10, 123858-123873.

[4] Deng, J., Chen, S., Zhao, Z., Qin, N., & Wang, Y. (2025). Fuzzy controller of shield posture based on particle swarm optimization algorithm. In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS) (pp. 614-620). IEEE.

[5] Fu, K., Qiu, D., Xue, Y., Shao, T., & Lan, G. (2024a). TBM tunneling strata automatic identification and working conditions decision support. Automation in Construction, 163, 105425.

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

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

[8] Kang, Q., Chen, E. J., Li, Z.-C., Luo, H.-B., & Liu, Y. (2023). Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling. Underground Space, 13, 335-350.

[9] Li, X., Pan, Y., Zhang, L., & Chen, J. (2023a). Dynamic and explainable deep learning-based risk prediction on adjacent building induced by deep excavation. Tunnelling and Underground Space Technology, 140, 105243.

[10] Li, Y., Pan, Y., & Zhang, L. (2024). Physics-guided deep learning for driving force estimation in synchronous tunnel boring machines under missing cylinders. Automation in Construction, 161, 105339.

[11] Li, Y., Sun, Q., Zhang, L., Wang, Z., & Feng, W. (2023b). Online multi-objective optimization for tunnel boring machine segment assembly considering stress concentration. Automation in Construction, 156, 105127.

[12] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.

[13] Qu, P., & Zhang, L. (2025). Uncertainty-based multi-objective optimization in twin tunnel design considering fluid-solid coupling. Reliability Engineering & System Safety, 253, 110575.

[14] Rastogi, D., Johri, P., Tiwari, V., & Elngar, A. A. (2024). Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross validation deep learning framework. Biomedical Signal Processing and Control, 88, 105602.

[15] Vaca-Rubio, C. J., Blanco, L., Pereira, R., & Caus, M. (2024). Kolmogorov-arnold networks (kans) for time series analysis. arXiv preprint arXiv:2405.08790.

[16] Wang, G., Huang, X., Bu, D., & Dai, Z. (2025). How large is the collapsed area of ground collapse induced by tunnelling. Geomechanics and Geoengineering, 20(1), 165-178.

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

[18] Wu, X., Wang, J., Feng, Z., Chen, H., Li, T., & Liu, Y. (2024). Multisource information fusion for real-time prediction and multiobjective optimization of large-diameter slurry shield attitude. Reliability Engineering & System Safety, 250, 110305.

[19] Xu, J., Bu, J., Qin, N., & Huang, D. (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.

[20] Zhang, L., Li, Y., Wang, L., Wang, J., & Luo, H. (2025). Physics-data driven multi-objective optimization for parallel control of TBM attitude. Advanced Engineering Informatics, 65, 103101.

[21] Zhou, H., Gao, Y., Zhang, C., Yang, F., Hu, M., Liu, H., & Jiang, Y. (2018). A 3D model of coupled hydro-mechanical simulation of double shield TBM excavation. Tunnelling and Underground Space Technology, 71, 1-14.

[22] Zhu, Y., Zhu, Y., Chen, E. J., Zhai, Y., 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.