Data augmentation-assisted muck image recognition during shield tunnelling

Abstract

This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling. The muck pictures were collected from the shield monitoring system above the conveyor belt. The data augmentation operations were then used to increase the quality of the original images. Furthermore, the Bayesian optimisation algorithm was employed to adjust the parameters of augmenters and highlight the features of the photos. The deep image recognition algorithms (AlexNet and GoogLeNet) were trained and enhanced by the augmentation images, which were used to establish the muck types identification models and assessed by the evaluation indices. Model efficiency was analysed through the performance and time cost of training and validation processes to select the optimal model for muck types identification. Results showed that the performance of identification models could be highly increased by data augmentation with Bayesian optimisation, and the enhanced GoogLeNet performed the highest efficiency for muck types identification.

Keywords

Muck types identification / Image recognition / Data augmentation / Bayesian optimisation / Shield tunnelling

Data availability

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

2 CRediT authorship contribution statement

Tao Yan: Writing - original draft, Methodology, Investigation, Data curation. Shui-Long Shen: Supervision, Methodology, Funding acquisition, Conceptualization. Annan Zhou: Writing - review & editing, Supervision, Investigation.

3 Declaration of competing interest

Annan Zhou is an editorial board member for Underground Space and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

4 Acknowledgement

The research work was funded by the Guangdong Provincial Basic and Applied Basic Research Fund Committee (2022A1515240073) and “The Pearl River Talent Recruitment Program” in 2019 (Grant No. 2019CX01G338), Guangdong Province, China.

References

[1] Arshi, O., & Mondal, S. (2023). Advancements in sensors and actuators technologies for smart cities: A comprehensive review. Smart Construction and Sustainable Cities, 1, 18.

[2] Asare, E. N., Affam, M., & Ziggah, Y. Y. (2023). A stacked generalisation methodology for estimating the uniaxial compressive strength of rocks. Smart Construction and Sustainable Cities, 1, 8.

[3] Ayawah, P. E. A., Sebbeh-Newton, S., Azure, J. W. A., Kaba, A. G. A., Anani, A., Bansah, S., & Zabidi, H. (2022). A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines. Tunnelling and Underground Space Technology, 125, 104497.

[4] Cheng, W. C., Bai, X. D., Sheil, B. B., Li, G., & Wang, F. (2020). Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines. Tunnelling and Underground Space Technology, 106, 103592.

[5] Ching, J., Wu, T. J., Stuedlein, A. W., & Bong, T. (2018). Estimating horizontal scale of fluctuation with limited CPT soundings. Geoscience Frontiers, 9(6), 1597-1608.

[6] Fu, X., Wu, M., Tiong, R. L. K., & Zhang, L. (2023). Data-driven realtime advanced geological prediction in tunnel construction using a hybrid deep learning approach. Automation in Construction, 146, 104672.

[7] Gustav, J. B., & Mark, D. F. (1999). Image lightness rescaling using sigmoidal contrast enhancement functions. Journal of Electronic Imaging, 8(4).

[8] Horé A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. In 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey (pp.2366-2369).

[9] Hu, M., Lu, J., Zhou, W., Xu, W., & Wu, Z. (2023). A multistage model for rapid identification of geological features in shield tunnelling. Scientific Reports, 13, 1799.

[10] Jin, D., Zhang, Z., & Yuan, D. (2021a). Effect of dynamic cutterhead on face stability in EPB shield tunneling. Tunnelling and Underground Space Technology, 110, 103827.

[11] Jin, D., Yuan, D., Li, X., & Su, W. (2021b). Probabilistic analysis of the disc cutter failure during TBM tunneling in hard rock. Tunnelling and Underground Space Technology, 109, 103744.

[12] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.

[13] Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11), 2278-2324.

[14] Li, B., Li, X. J., Xiao, W., Cheng, Q., & Bao, T. (2023). Quantitative characterization method of 3D roughness of rock mass structural surface considering size effect. Smart Construction and Sustainable Cities, 1, 9.

[15] Liu, M., Liao, S., Yang, Y., Men, Y., He, J., & Huang, Y. (2021). Tunnel boring machine vibration-based deep learning for the ground identification of working faces. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1340-1357.

[16] Liu, M., Liao, S., Men, Y., Xing, H., & Sun, L. (2022). Field monitoring of TBM vibration during excavating changing stratum: patterns and ground identification. Rock Mechanics and Rock Engineering, 55, 1481-1498.

[17] Qi, X., Li, D. Q., Phoon, K. K., Cao, Z., & Tang, X. (2016). Simulation of geologic uncertainty using coupled Markov chain. Engineering Geology, 207, 129-140.

[18] Qin, C., Huang, G., Yu, H., Wu, R., Tao, J., & Liu, C. (2023a). Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction. Geoscience Frontiers, 14(2), 101519.

[19] Qin, S., Cheng, Y., & Zhou, W. H. (2023b). State-of-the-art review on pressure infiltration behavior of bentonite slurry into saturated sand for TBM tunneling. Smart Construction and Sustainable Cities, 1, 14.

[20] Shi, C., & Wang, Y. (2021). Development of subsurface geological crosssection from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. Journal of Geotechnical and Geoenvironmental Engineering, 147(9), 04021082.

[21] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1-9).

[22] Thum, L., & Paoli, D. R. (2015). 2D and 3D GIS-based geological and geomechanical survey during tunnel excavation. Engineering Geology, 192, 19-25.

[23] Wang, Q., Luo, L., Xie, H., Rao, Y., Lau, R. Y. K., & Zhang, D. (2022). A deep data augmentation framework based on generative adversarial networks. Multimedia Tools and Applications, 81, 42871-42887.

[24] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612.

[25] Wei, L., Khan, M., Mehmood, O., Dou, Q., Bateman, C., Magee, D. R., & Cohn, A. G. (2019). Web-based visualisation for look-ahead ground imaging in tunnel boring machines. Automation in Construction, 105, 102830.

[26] Xu, D., Wang, Y., Huang, J., Liu, S., Xu, S., & Zhou, K. (2023). Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms. Tunnelling and Underground Space Technology, 131, 104852.

[27] Yang, Z., He, B., Liu, Y., Wang, D., & Zhu, G. (2021). Classification of rock fragments produced by tunnel boring machine using convolutional neural networks. Automation in Construction, 125, 103612.

[28] Yu, H., Tao, J., Qin, C., Liu, M., Xiao, D., Sun, H., & Liu, C. (2022). A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing, 165.

[29] Zhang, C., Liang, M., Song, X., Liu, L., Wang, H., Li, W., & Shi, M. (2022a). Generative adversarial network for geological prediction based on TBM operational data. Mechanical Systems and Signal Processing, 162, 108035.

[30] Zhang, D. M., Fu, L., Huang, H. W., Wu, H. M., & Li, G. (2022b). Deep learning-based automatic detection of muck types for earth pressure balance shield tunneling in soft ground. Computer-Aided Civil and Infrastructure Engineering, 1-16.

[31] Zhang, W., Wu, C., Li, Y., Wang, L., & Samui, P. (2021). Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(1), 27-40.

[32] Zhao, L. S., Zhuo, S., & Shen, B. i. (2023). An efficient model to estimate the soil profile and stratigraphic uncertainty quantification. Engineering Geology, 315, 107025.

[33] Zhou, C., Kong, T., Zhou, Y., Zhang, H., & Ding, L. (2019). Unsupervised spectral clustering for shield tunneling machine monitoring data with complex network theory. Automation in Construction, 107, 102924.