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The Innovation Informatics > 2026 Vol. 2 > No. 1 > 100019
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Machine learning–based TBM parameter prediction: Methodological reflections from an engineering perspective

  • 1.

    College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

  • 2.

    Department of Structural Engineering, University of California, San Diego, La Jolla 92093, USA

  • 3.

    College of Engineering, Ocean University of China, Qingdao 266100, China

  • 4.

    School of Mathematics and Physics, The University of Queensland, St Lucia 4072, Australia

  • 5.

    China Railway No.3 Engineering Group Co., Ltd., Taiyuan 030006, China

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The Innovation Informatics  2(1),  https://doi.org/10.59717/j.xinn-inform.2026.100019  |  Cite this article
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    • REFERENCES
  • [1] Huang G., Qin C., Wang H., et al. (2025). TBM rock fragmentation classification using an adaptive spot denoising and contour-texture decomposition attention-based method. Tunn. Undergr. Space Technol. 161:106498. DOI:10.1016/j.tust.2025.106498

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    [4] Man K., Wu L., Liu X., et al. (2024). Prediction of rock mass classification in tunnel boring machine tunneling using a PCA–GRU neural network. Deep Undergr. Sci. Eng. 3:413−425. DOI:10.1002/dug2.12084

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  • Cite this article:

    Lyu X., Wu H., Lu W., et al. (2026). Machine learning–based TBM parameter prediction: Methodological reflections from an engineering perspective. The Innovation Informatics 2:100019. https://doi.org/10.59717/j.xinn-inform.2026.100019
    Lyu X., Wu H., Lu W., et al. (2026). Machine learning–based TBM parameter prediction: Methodological reflections from an engineering perspective. The Innovation Informatics 2:100019. https://doi.org/10.59717/j.xinn-inform.2026.100019

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