Towards Real Time Ground Forecast for TBM Tunneling: Finding Label Errors in Data Sets
Publication: Geo-Risk 2023
ABSTRACT
Tunnel construction using tunnel boring machines (TBM) is associated with unforeseen ground conditions. There have been several attempts to use data recorded by the TBM to predict ground conditions ahead of the tunnel face and assist in the automation of the tunneling process. One of the main issues associated with such data, in particular for closed face machines, is that the operator has no view of the ground ahead, compromising ground labelling, which is often estimated from face mappings done at spaced intervals. The use of these “noisy” labels to train ground forecast models affects their performance. In this paper, confident learning (CL) is applied to investigate and detect label errors in a data set from a TBM tunnel at the Porto Metro Project, in Portugal. The detected mislabeled points are further investigated to determine the possible reasons for their mislabeling. Limitations, challenges, and directions for future research are discussed at the end.
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REFERENCES
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, New York.
Chan, M. H. C. (1981). A geological prediction and updating model in tunneling. M.S. thesis, Massachusetts Institute of Technology. URI: http://hdl.handle.net/1721.1/15886.
Hoek, E. (1994). “Strengths of rock and rock masses” ISRM News Journal, 2(2): 4–16.
Hou, S., Y. Liu, and Q. Yang. (2021). “Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning.” Journal of Rock Mechanics and Geotechnical Engineering. Elsevier BV. https://doi.org/10.1016/j.jrmge.2021.05.004.
Northcutt, C. G., A. Athalye, and J. Mueller. (2021a). “Pervasive label errors in test sets destabilize machine learning benchmarks.” arXiv.org, <https://arxiv.org/abs/2103.14749>(Sep. 6, 2022).
Northcutt, C. G., L. Jiang, and I. L. Chuang. (2021b). Confident Learning: Estimating Uncertainty in Dataset Labels. Journal of Artificial Intelligence Research.
Sheil, B. B., S. K. Suryasentana, M. A. Mooney, and H. Zhu. (2020). Machine learning to inform tunnelling operations: recent advances and future trends. Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction 173, 74–95. https://doi.org/10.1680/jsmic.20.00011.
Sousa, R. L., and H. H. Einstein. (2012). Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study. Tunnelling and Underground Space Technology 27, 86–100. https://doi.org/10.1016/j.tust.2011.07.003.
Suwansawat, S., and H. H. Einstein. (2006). “Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling.” Tunnelling and Underground Space Technology, 21 (2): 133–150. https://doi.org/10.1016/j.tust.2005.06.007.
Wu, Z., R. Wei, Z. Chu, and Q. Liu. (2021). “Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method.” Journal of Rock Mechanics and Geotechnical Engineering. Chinese Academy of Sciences. https://doi.org/10.1016/j.jrmge.2021.07.012.
Zhang, Q., Z. Liu, and J. Tan. (2019). Prediction of geological conditions for a tunnel boring machine using big operational data. Automation in Construction 100, 73–83. https://doi.org/10.1016/j.autcon.2018.12.022.
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Geo-Risk 2023
Pages: 147 - 153
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Published online: Jul 20, 2023
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