Volume 40 Issue 2
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Article Navigation >  GEOTECHNICAL ENGINEERING TECHNIQUE  > 2026  >  40(2): 175-184
XU Min, YU Wang, FU Helin, CAO Guiqian, LI Jun. Tunnel surrounding rock classification and application based on face image recognition[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2026, 40(2): 175-184. doi: 10.20265/j.cnki.issn.1007-2993.2025-0053
Citation: XU Min, YU Wang, FU Helin, CAO Guiqian, LI Jun. Tunnel surrounding rock classification and application based on face image recognition[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2026, 40(2): 175-184. doi: 10.20265/j.cnki.issn.1007-2993.2025-0053
shu

Tunnel surrounding rock classification and application based on face image recognition

doi: 10.20265/j.cnki.issn.1007-2993.2025-0053
  • 1.

    Yueyang City Operation Investment Group Co., Ltd., Yueyang 414000, Hunan, China

  • 2.

    The 23rd Metallurgical Construction Group Co., Ltd. of Minmetals, Changsha 410116, Hunan, China

  • 3.

    Central South University, Changsha 410075, Hunan, China

  • Received Date: 2025-02-10
  • Accepted Date: 2025-06-26
  • Rev Recd Date: 2025-04-18
    • Available Online: 2026-04-09
  • Publish Date: 2026-04-09
    Abstract
  • The stability of surrounding rock during tunnel excavation is crucial, and support design relies on accurate classification of the surrounding rock quality. To achieve rapid and precise classification during tunnel construction, an intelligent and rapid classification method for tunnel surrounding rock was proposed, based on the YOLOv8 deep learning algorithm and digital image processing technology, focusing on the tunnel face and incorporating the modified BQ method. The results indicate that the YOLOv8 deep learning model can accurately identify and locate fractures in tunnel face photographs. Combining with image processing techniques, it effectively extracts fracture information from the face, thereby assessing the integrity of the tunnel face and enabling rapid classification of the surrounding rock. Field validation in Yueyang Laimipo Tunnel demonstrates that, compared to actual surrounding rock grades, this method achieves a prediction accuracy of 90%, meeting the needs for rapid classification during tunnel construction. The research provides a reference for dynamic classification of tunnel surrounding rock grades and offers guidance for tunnel excavation and support.

     

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