Paper contribution

Intelligent rock mass classification for different TBM projects

Chenfeng Gao; Shaokang Hou; Songyu Yue; Chenyao Wei; Yaoru Liu

Proceedings
EUROCK 2026
Pages
1311-1316
Submitted
28 January 2026
Accepted
15 June 2026
Published
12 August 2026

Summary

Abstract

Accurate classification of surrounding rock grades during Tunnel Boring Machine (TBM) operations is essential to improve tunnelling efficiency and ensure construction safety. However, traditional classification methods face significant challenges, particularly in leveraging existing TBM engineering data to classify surrounding rock in new TBM projects—an important research focus in the field. This study proposes an intelligent cross-project TBM rock classification method based on data from two different TBM projects. Firstly, the raw TBM engineering data are preprocessed by eliminating invalid rock-breaking data, and effective tunnelling cycles are segmented to extract the stable section tunnelling data. Subsequently, considering the engineering design parameters (including tunnel diameter and number of cutters), the conversion relationships of key tunneling parameters are established based on TBM tunnelling mechanics. Adopting the data from Project A, an intelligent classification model is developed and trained with key tunnelling parameters as inputs and surrounding rock grades as outputs. Finally, the key tunnelling parameters from Project B are converted into model inputs, and the classification results are analysed and discussed. The results indicate that applying the conversion rules to the key tunnelling parameters of Project B improved the prediction accuracy of surrounding rock grades by 2%–5%.

Contributors

Authors

  1. Chenfeng Gao Tsinghua University ORCID 0000-0001-6703-6604
  2. Shaokang Hou China Renewable Energy Engineering Institute
  3. Songyu Yue Tsinghua University
  4. Chenyao Wei Tsinghua University
  5. Yaoru Liu Tsinghua University

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References

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