A coal seam roadway surrounding rock stability evaluation method based on DBO-PPM and machine learning

By combining DBO-PPM with machine learning, the problems of data integration and model optimization in the evaluation of the surrounding rock stability of coal seam roadways were solved, achieving high-precision prediction and visualization of the roadway surrounding rock stability, and providing a scientific basis for support design.

CN122132866APending Publication Date: 2026-06-02CHINA UNIV OF MINING & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNIV OF MINING & TECH
Filing Date
2026-03-03
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies for evaluating the stability of surrounding rock in coal seam roadways suffer from insufficient data source integration, incomplete evaluation index system, and inadequate model parameter optimization, making it difficult to meet the requirements for accurate evaluation under complex geological conditions.

Method used

A method based on DBO-PPM and machine learning was adopted. Multi-source data was integrated through Geographic Information System (GIS) to construct an adaptive grid division and evaluation index system. The DBO algorithm and various machine learning algorithms, such as radial basis function classifier, XGBoost and Stacking ensemble model, were combined to predict the stability of the surrounding rock of the tunnel and visualized using ArcGIS.

Benefits of technology

It enables high-precision evaluation of surrounding rock stability under complex geological conditions, reduces the risk of misjudgment, and provides intuitive support optimization and emergency decision-making basis.

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Abstract

This invention relates to the field of surrounding rock stability evaluation technology, providing a method for evaluating the stability of coal seam roadways based on DBO-PPM and machine learning. The method involves adaptively gridding the roadway area to be evaluated and extracting multi-source stability influencing factors. DBO is introduced to globally optimize the projection direction of PPM, obtaining objective and accurate evaluation index weights. The K-means algorithm is combined to achieve automatic sample classification. The Smote algorithm is used to oversample scarce accident samples, constructing a class-balanced evaluation index system. Machine learning models, including a radial basis function classifier optimized with GridSearchCV hyperparameters, XGBoost, and Stacking models, are constructed for accurate prediction and visualization using ArcGIS. This invention improves the accuracy and efficiency of surrounding rock stability evaluation in complex geological conditions, providing a scientific basis for support optimization and disaster early warning in deep mines.
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