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Roadway roof stability grading method based on multi-index optimization.

Xiaoqiang Xue1, Wei Du1, Jian Cui2

  • 1Shaanxi Xiaobaodang Mining Co., Ltd., Yulin, 719000, China.

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|August 9, 2025
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Summary
This summary is machine-generated.

A new method grades roadway rock stability for optimized support design. Key factors like depth and roof strength influence stability, with a BP neural network model achieving over 91% accuracy in classification.

Keywords:
BP neural networkBorehole dataCoal mine safetyEnclosure stability classificationHierarchical analysis methodNumerical simulation

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Area of Science:

  • Mining Engineering
  • Geotechnical Engineering
  • Rock Mechanics

Background:

  • Current roadway support strategies are often uniform, leading to stress issues in unstable zones and over-support in stable areas.
  • A need exists for a reliable rock mass classification system to enable tailored support designs throughout roadways.
  • The study focuses on the Xiaobaodang No.1 coal mine to address these challenges.

Purpose of the Study:

  • To develop and validate a robust method for classifying roadway perimeter rock stability.
  • To enable differentiated and optimized support designs for improved roadway safety and efficiency.
  • To provide theoretical references for subsequent differentiated support strategies in mining operations.

Main Methods:

  • Data analysis using SPSS and the hierarchical analysis method to determine weights of ten influential factors.
  • Development of a rock stability classification formula based on weighted influencing factors and normalized borehole data.
  • Construction and training of a Backpropagation (BP) neural network model for roadway roof stability classification.

Main Results:

  • Five key factors significantly affecting roof stability were identified: roadway depth, roof strength, direct roof thickness, mining height, and rock integrity.
  • The BP neural network model achieved high accuracy: 92.8% for the training set and 91.7% for the test set.
  • Mine pressure data validated the model's classification, showing distinct changes corresponding to stability levels, and numerical simulations indicated higher stress in areas prone to collapse.

Conclusions:

  • The proposed rock stability classification method, utilizing a BP neural network, provides a reliable approach for differentiated roadway support.
  • Understanding the influence of key geological and mining parameters is crucial for effective support design.
  • Strengthened support is necessary in areas identified as highly prone to roof collapse based on stress analysis.