Data-driven real-time prediction for attitude and position of super-large diameter shield using a hybrid deep learning approach

Abstract

The presented research introduces a novel hybrid deep learning approach for the dynamic prediction of the attitude and position of super-large diameter shields - a critical consideration for construction safety and tunnel lining quality. This study proposes a hybrid deep learning approach for predicting dynamic attitude and position prediction of super-large diameter shield. The approach consists of principal component analysis (PCA) and temporal convolutional network (TCN). The former is used for employing feature level fusion based on features of the shield data to reduce uncertainty, improve accuracy and the data effect, and 9 sets of required principal component characteristic data are obtained. The latter is adopted to process sequence data in predicting the dynamic attitude and position for the advantages and potential of convolution network. The approach’s effectiveness is exemplified using data from a tunnel construction project in China. The obtained results show remarkable accuracy in predicting the global attitude and position, with an average error ratio of less than 2 mm on four shield outputs in 97.30% of cases. Moreover, the approach displays strong performance in accurately predicting sudden fluctuations in shield attitude and position, with an average prediction accuracy of 89.68%. The proposed hybrid model demonstrates superiority over TCN, long short-term memory (LSTM), and recurrent neural network (RNN) in multiple indexes. Shapley additive exPlanations (SHAP) analysis is also performed to investigate the significance of different data features in the prediction process. This study provides a real-time warning for the shield driver to adjust the attitude and position of super-large diameter shields.

Keywords

Shield attitude and position / Super-large diameter shield / PCA-TCN / Deep learning / Real-time warning

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.

Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (Grant Nos. 52078304, 51938008, 52090084, and 52208354); Guangdong Province Key Field R&D Program Project (Grant Nos. 2019B111108001 and 2022B0101070001); Shenzhen Fundamental Research (Grant No. 20220525163716003); and the Pearl River Delta Water Resources Allocation Project (CD88-GC022020-0038).

References

[1] Alajmi, M. S., & Almeshal, A. M. (2020). Predicting the tool wear of a drilling process using novel machine learning xgboost-sda. Materials, 13, 4952.

[2] Avunduk, E., Tumac¸ D., & Atalay, A. (2014). Prediction of roadheader performance by artificial neural network. Tunnelling and Underground Space Technology, 44, 3-9.

[3] Bai, S., Kolter, J.Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

[4] Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5, 157-166.

[5] Chen, E. J., Chen, Y.-Y., Wei, L.-C., & Luo, H.-B. (2021a). Uncertainty and sensitivity analysis of significant parameters for superlarge diameter shield excavation. Advances in Civil Engineering, 2021.

[6] Chen, F., Jiang, S., Xiong, H., Yin, Z.-Y., & Chen, X. (2023a). Micro pore analysis of suffusion in filter layer using tri-layer cfd-dem model. Computers and Geotechnics, 156, 105303.

[7] Chen, F., Xiong, H., Wang, X., & Yin, Z.-Y. (2023b). Transmission effect of eroded particles in suffusion using the cfd-dem coupling method. Acta Geotechnica, 18, 335-354.

[8] Chen, F., Xiong, H., Yin, Z.-Y., & Chen, X. (2023c). Impermeable and mechanical stability of filter cake under different infiltration conditions via cfd-dem. Acta Geotechnica, 1-26.

[9] Chen, J., Yang, T., Zhang, D., Huang, H., & Tian, Y. (2021b). Deep learning based classification of rock structure of tunnel face. Geoscience Frontiers, 12, 395-404.

[10] Daffertshofer, A., Lamoth, C. J., Meijer, O. G., & Beek, P. J. (2004). Pca in studying coordination and variability: a tutorial. Clinical Biomechanics, 19, 415-428.

[11] De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The mahalanobis distance. Chemometrics and Intelligent Llaboratory Systems, 50, 1-18.

[12] Elbaz, K., Yan, T., Zhou, A., & Shen, S.-L. (2022). Deep learning analysis for energy consumption of shield tunneling machine drive system. Tunnelling and Underground Space Technology, 123, 104405.

[13] Fu, X., Wu, M., Ponnarasu, S., & Zhang, L. (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.

[14] Fu, Y., Zeng, D., Xiong, H., Li, X., & Chen, Y. (2022). Seepage effect on failure mechanisms of the underwater tunnel face via cfd-dem coupling. Computers and Geotechnics, 146, 104591.

[15] Gajjar, S., & Palazoglu, A. (2016). A data-driven multidimensional visualization technique for process fault detection and diagnosis. Chemometrics and Intelligent Laboratory Systems, 154, 122-136.

[16] Gao, B., Wang, R., Lin, C., Guo, X., Liu, B., & Zhang, W. (2021). Tbm penetration rate prediction based on the long short-term memory neural network. Underground Space, 6, 718-731.

[17] Gao, M.-Y., Zhang, N., Shen, S.-L., & Zhou, A. (2020). Real-time dynamic earth-pressure regulation model for shield tunneling by integrating gru deep learning method with ga optimization. IEEE Access, 8, 64310-64323.

[18] Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with lstm. Neural Computation, 12, 2451-2471.

[19] Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L., & Maggio, R. M. (2018). Use of principal component analysis (pca) and hierarchical cluster analysis (hca) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science & Technology, 72, 83-90.

[20] Guan, Q., & Yang, Z. (2022). Hybrid deep learning model for prediction of monotonic and cyclic responses of sand. Acta Geotechnica, 1-15.

[21] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural Computation, 9.

[22] Huang, H., Chang, J., Zhang, D., Zhang, J., Wu, H., & Li, G. (2022). Machine learning-based automatic control of tunneling posture of shield machine. Journal of Rock Mechanics and Geotechnical Engineering, 14, 1153-1164.

[23] Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685-695.

[24] Lai, J., Qiu, J., Feng, Z., Chen, J., & Fan, H. (2016). Prediction of soil deformation in tunnelling using artificial neural networks. Computational Intelligence and Neuroscience, 16.

[25] Lee, S., Ha, J., Zokhirova, M., Moon, H., & Lee, J. (2018). Background information of deep learning for structural engineering. Archives of Computational Methods in Engineering, 25, 121-129.

[26] Li, D., Liu, Z., Xiao, P., Zhou, J., & Armaghani, D. J. (2022). Intelligent rockburst prediction model with sample category balance using feedforward neural network and bayesian optimization. Underground Space, 7, 833-846.

[27] Liao, S.-M., Liu, J.-H., Wang, R.-L., & Li, Z.-M. (2009). Shield tunneling and environment protection in shanghai soft ground. Tunnelling and Underground Space Technology, 24, 454-465.

[28] Lin, S.-S., Shen, S.-L., Zhang, N., & Zhou, A. (2021). Modelling the performance of epb shield tunnelling using machine and deep learning algorithms. Geoscience Frontiers, 12, 101177.

[29] Liu, H., Wang, J., Zhang, L., & Zhao, G. (2014). Trajectory tracking of hard rock tunnel boring machine with cascade control structure. In Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference (pp. 2326-2331). IEEE.

[30] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.

[31] Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H. H., Abdulhamid, S. N., Ali, H. F. H., Hasan, A. M., Khishe, M., & Mahmud, H. (2021). Machine learning forecasting models of disc cutters life of tunnel boring machine. Automation in Construction, 128, 103779.

[32] Romeu, P., Zamora-Martıónez, F., Botella-Rocamora, P., & Pardo, J. (2015). Stacked denoising auto-encoders for short-term time series forecasting. In Artificial Neural Networks (pp. 463-486).

[33] Springer. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.

[34] Salehi, H., & Burguen˜o, R. (2018). Emerging artificial intelligence methods in structural engineering. Engineering Structures, 171, 170-189.

[35] Shan, F., He, X., Armaghani, D. J., Zhang, P., & Sheng, D. (2022). Success and challenges in predicting tbm penetration rate using recurrent neural networks. Tunnelling and Underground Space Technology, 130, 104728.

[36] Shen, S.-L., Elbaz, K., Shaban, W. M., & Zhou, A. (2022). Real-time prediction of shield moving trajectory during tunnelling. Acta Geotechnica, 17, 1533-1549.

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

[38] 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.

[39] Wang, X., Lu, H., Wei, X., Wei, G., Behbahani, S. S., & Iseley, T. (2020). Application of artificial neural network in tunnel engineering: A systematic review. IEEE Access, 8, 119527-119543.

[40] Wu, L., Zhang, X., Wang, W., Meng, X., & Guo, H. (2021). Comparison of dynamic characteristics between small and super-large diameter cross-river twin tunnels under train vibration. Applied Sciences, 11, 7577.

[41] Xiao, H., Xing, B., Wang, Y., Yu, P., Liu, L., & Cao, R. (2021). Prediction of shield machine attitude based on various artificial intelligence technologies. Applied Sciences, 11, 10264.

[42] Xie, H., Duan, X., Yang, H., & Liu, Z. (2012). Automatic trajectory tracking control of shield tunneling machine under complex stratum working condition. Tunnelling and Underground Space Technology, 32, 87-97.

[43] Xiong, H., Qiu, Y., Lin, X.-T., Chen, X., & Huang, D. (2023). Multiple arching in cohesion-friction soils: Insights from deformation behavior and failure mechanisms using fem-sph approach. Computers and Geotechnics, 154, 105146.

[44] Xiong, H., Yin, Z.-Y., & Nicot, F. (2020). Programming a micromechanical model of granular materials in julia. Advances in Engineering Software, 145, 102816.

[45] Xiong, H., Zhang, Z., Sun, X., Yin, Z.-Y., & Chen, X. (2022). Clogging effect of fines in seepage erosion by using cfd-dem. Computers and Geotechnics, 152, 105013.

[46] Yang, H., Shi, H., Gong, G., & Hu, G. (2009). Electro-hydraulic proportional control of thrust system for shield tunneling machine. Automation in Construction, 18, 950-956.

[47] Yang, J., Zhang, D., Frangi, A. F., & Yang, J.-Y. (2004). Twodimensional pca: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 131-137.

[48] Yang, K., Ahn, C. R., Vuran, M. C., & Kim, H. (2017). Collective sensing of workers’ gait patterns to identify fall hazards in construction. Automation in Construction, 82, 166-178.

[49] Yang, Y., Zhou, B., Xie, X., & Liu, C. (2018). Characteristics and causes of cracking and damage of shield tunnel segmented lining in construction stage-a case study in shanghai soft soil. European Journal of Environmental and Civil Engineering, 22, s213-s227.

[50] Ye, X.-W., Jin, T., & Chen, P.-Y. (2019). Structural crack detection using deep learning-based fully convolutional networks. Advances in Structural Engineering, 22, 3412-3419.

[51] Yu, F., & Koltun, V. (2016). Multi-scale context aggregation by dilated convolutions. arxiv 2015. arXiv preprint arXiv:1511.07122,615.

[52] 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.

[53] Zhang, P., Jin, Y.-F., & Yin, Z.-Y. (2021a). Machine learning-based uncertainty modelling of mechanical properties of soft clays relating to time-dependent behavior and its application. International Journal for Numerical and Analytical Methods in Geomechanics, 45, 1588-1602.

[54] Zhang, P., Wu, H.-N., Chen, R.-P., Dai, T., Meng, F.-Y., & Wang, H.-B. (2020). A critical evaluation of machine learning and deep learning in shield-ground interaction prediction. Tunnelling and Underground Space Technology, 106, 103593.

[55] Zhang, P., Yin, Z.-Y., & Jin, Y.-F. (2022b). Bayesian neural networkbased uncertainty modelling: application to soil compressibility and undrained shear strength prediction. Canadian Geotechnical Journal, 59, 546-557.

[56] Zhang, P., Yin, Z.-Y., Jin, Y.-F., & Sheil, B. (2022c). Physics-constrained hierarchical data-driven modelling framework for complex pathdependent behaviour of soils. International Journal for Numerical and Analytical Methods in Geomechanics, 46, 1831-1850.

[57] Zhang, P., Yin, Z.-Y., Jin, Y.-F., Yang, J., & Sheil, B. (2022d). Physicsinformed multifidelity residual neural networks for hydromechanical modeling of granular soils and foundation considering internal erosion. Journal of Engineering Mechanics, 148, 04022015.

[58] Zhang, P., Yin, Z.-Y., & Sheil, B. (2023). A physics-informed data-driven approach for consolidation analysis. Géotechnique, 1-12.

[59] Zhang, W., Li, H., Wu, C., Li, Y., Liu, Z., & Liu, H. (2021b). Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling. Underground Space, 6, 353-363.

[60] Zhang, W., Li, Y., Wu, C., Li, H., Goh, A., & Liu, H. (2022e). Prediction of lining response for twin tunnels constructed in anisotropic clay using machine learning techniques. Underground Space, 7, 122-133.

[61] Zhang, Z., & Ma, L. (2018). Attitude correction system and cooperative control of tunnel boring machine. International Journal of Pattern Recognition and Artificial Intelligence, 32, 1859018.

[62] Zhou, B., Gui, Y., Xie, X., Li, W., & Li, Q. (2022). A measurable evaluation method of visual comfort in underground space by intelligent sorting and classification algorithms. Underground Space, 7, 453-464.

[63] Zhou, C., Xu, H., Ding, L., Wei, L., & Zhou, Y. (2019). Dynamic prediction for attitude and position in shield tunneling: A deep learning method. Automation in Construction, 105, 102840.

[64] Zhou, Z., Zhang, J., Gong, C., & Wu, W. (2023). Automatic tunnel lining crack detection via deep learning with generative adversarial networkbased data augmentation. Underground Space, 9, 140-154.