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Updated: Jul 15, 2026

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Real-time grading method of tunnel surrounding rock based on image recognition.

Yihuan Xiao1, Hao Yuan1, Qingye Shi1

  • 1School of Civil Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China.

Frontiers in Artificial Intelligence
|February 23, 2026
PubMed
Summary

This study introduces a real-time method using image processing and lightweight deep learning for grading tunnel surrounding rock. The automated system achieves high accuracy in classifying lithology and rock-mass integrity, proving its practical value for construction.

Keywords:
ShuffleNetV2image processingmachine learningsurrounding rock classificationtunnel engineering

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Area of Science:

  • Geotechnical Engineering
  • Artificial Intelligence in Civil Engineering

Background:

  • Accurate grading of tunnel surrounding rock is crucial for construction safety and efficiency.
  • Traditional methods for rock grading can be time-consuming and subjective.

Purpose of the Study:

  • To develop a rapid and accurate real-time grading method for tunnel surrounding rock.
  • To integrate image processing with lightweight deep learning for automated rock classification.

Main Methods:

  • An automated pipeline was developed combining image processing and machine learning algorithms.
  • Image preprocessing included grayscale conversion, denoising, enhancement, and normalization.
  • A ShuffleNetV2 convolutional neural network model was optimized for lithology identification and rock-mass property sensing.

Main Results:

  • The method achieved 85% accuracy for lithology classification.
  • Rock-mass integrity was classified with 75% accuracy.
  • Overall surrounding-rock grade was achieved with 80% accuracy on an engineering site.

Conclusions:

  • The proposed real-time grading method demonstrates feasibility and practical value for tunnel construction.
  • The integration of image processing and deep learning offers theoretical insights for scientific evaluation of tunnel surrounding rock.