Abstract
Tunnel boring machine (TBM) is widely utilized in large cross-section tunnel excavation with high permeability soil conditions. During the shield tunneling process of TBM, dynamic prediction of driving parameters including advance rate and cutterhead torque is required. This study establishes a dynamic prediction model (DPM), LSTM-XGBoost, based on machine learning and deep learning algorithm framework incorporating with wavelet transform, long short-term memory method (LSTM) and extreme gradient boosting method (XGBoost). The proposed DPM can predict TBM-driving parameters in advance and adjust parameter weight in order to optimize operation. Moreover, the generalization ability of the proposed DPM is compared with traditional machine learning algorithms such as support vector machines (SVM) based on the data obtained in a practical tunneling project. The comparison results reveal that the LSTM-based DPM is suitable for prediction of time-series data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bhagat, S.K., Tiyasha, T., Awadh, S.M., Tung, T.M., Jawad, A.H., Yaseen, Z.M.: Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models. Environ. Pollut. 268, 115663 (2021)
Chen, G., Li, Q.Y., Li, D.Q., Wu, Z.Y., Liu, Y.: Main frequency band of blast vibration signal based on wavelet packet transform. Appl. Math. Model. 74, 569–585 (2019)
Elbaz, K., Yan, T., Zhou, A., Shen, S.L.: Deep learning analysis for energy consumption of shield tunneling machine drive system. Tunn. Undergr. Space Technol. 123, 104405 (2022)
Liu, Y., Jiang, Y., Lee, F.H.: Towards a design framework for spatial variability in cement treatment for underground construction. Geotech. Eng. 47(3), 79–84 (2016)
Mahdevari, S., Haghighat, H.S., Torabi, S.R.: A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunn. Undergr. Space Technol. 38, 59–68 (2013)
Nyéki, A., et al.: Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods. Precision Agric. 22(5), 1397–1415 (2021). https://doi.org/10.1007/s11119-021-09833-8
Pan, Y., Liu, Y., Tyagi, A., Lee, F.H., Li, D.Q.: Model-independent strength-reduction factor for effect of spatial variability on tunnel with improved soil surrounds. Géotechnique 71(5), 406–422 (2021)
Wang, M.X., Huang, D., Wang, G., Li, D.Q.: SS-XGBoost: a machine learning framework for predicting Newmark sliding displacements of slopes. J. Geotech. Geoenviron. Eng. 146(9), 04020074 (2020)
Wang, R.H., Li, D.Q., Chen, E.J., Liu, Y.: Dynamic prediction of mechanized shield tunneling performance. Autom. Constr. 132, 103958 (2021)
Wang, R.H., Li, D.Q., Wang, M.Y., Liu, Y.: Deterministic and probabilistic investigations of piping occurrence during tunneling through spatially variable soils. ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 7(2), 04021009 (2021)
Yagiz, S., Gokceoglu, C., Sezer, E., Iplikci, S.: Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng. Appl. Artif. Intell. 22(4–5), 808–814 (2009)
Zhang, L., Liu, Y.: Numerical investigations on the seismic response of a subway tunnel embedded in spatially random clays. Underground Space 5(1), 43–52 (2020)
Zhang, P., Wu, H.N., Chen, R.P., Dai, T., Meng, F.Y., Wang, H.B.: A critical evaluation of machine learning and deep learning in shield-ground interaction prediction. Tunn. Undergr. Space Technol. 106, 103593 (2020)
Zhao, Y., Gong, Q., Tian, Z., Zhou, S., Jiang, H.: Torque fluctuation analysis and penetration prediction of EPB TBM in rock–soil interface mixed ground. Tunn. Undergr. Space Technol. 91, 103002 (2019)
Zhou, X.P., Zhai, S.F.: Estimation of the cutterhead torque for earth pressure balance TBM under mixed-face conditions. Tunn. Undergr. Space Technol. 74, 217–229 (2018)
Acknowledgement
This research is supported by the National Natural Science foundation of China (Grant No. 52079099).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, R., Chen, G., Liu, Y. (2023). A Dynamic Model of Machine Learning and Deep Learning in Shield Tunneling Parameters Prediction. In: Geng, G., Qian, X., Poh, L.H., Pang, S.D. (eds) Proceedings of The 17th East Asian-Pacific Conference on Structural Engineering and Construction, 2022. Lecture Notes in Civil Engineering, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-7331-4_99
Download citation
DOI: https://doi.org/10.1007/978-981-19-7331-4_99
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7330-7
Online ISBN: 978-981-19-7331-4
eBook Packages: EngineeringEngineering (R0)