收藏切换
Intelligent Prediction of Thickness of the Surround Rock Loosened Zone in Roadways Based on Interpretable Machine Learning
收藏切换
Quanming ZHANG1, Wei LIU1, Jingxiao TANG1, Hao LIU2, Ke ZHAI1
Mining Research and Development | 2025, 45(12) : 106 - 114
Less
收藏切换
Mining Research and Development | 2025, 45(12): 106-114
Intelligent Prediction of Thickness of the Surround Rock Loosened Zone in Roadways Based on Interpretable Machine Learning
Full
Quanming ZHANG1, Wei LIU1, Jingxiao TANG1, Hao LIU2, Ke ZHAI1
Affiliations
  • 1.Yanzhou Mining Group Co., Ltd., Xinglongzhuang Coal Mine, Jining, Shandong 272102, China
  • 2.Laboratory of Mine Disaster Prevention and Control, Shandong University of Science and Technology, Taian, Shandong 271019, China
Published: 2025-12-25
Outline
收藏切换

To enhance the accuracy and reliability of measuring the thickness of the surrounding rock loosened zone in roadways, a high-precision prediction model (PSO-Stacking) for the thickness of the loosened zone was proposed. This model employed interpretable artificial intelligence algorithms to address the black-box effect of machine learning algorithms and explained the prediction mechanism. The model was validated through an engineering case study at Xinglongzhuang Coal Mine. By incorporating SHAP analysis to interpret model decisions, the influence of surrounding rock loosened zone characteristics was revealed, thereby improving interpretability. The research results demonstrate that the PSO-Stacking model performs exceptionally well in predicting the thickness of the loosened zone, with high prediction accuracy and minimal error. On the test set, the R2 value reached 0.957, while the RMSE and MAE were 0.118 and 0.087, respectively. Global interpretability analysis of the prediction model indicates that roadway burial depth and joint development degree have the most significant impact on the prediction results. Local interpretability analysis reveals that burial depth considerably influences the thickness of the loosened zone in high-strength surrounding rock, while joint development has a lesser effect. According to the application in Xinglongzhuang Coal Mine, the average error between the predicted results and the actual values is only 4.31%, demonstrating that the proposed model can effectively predict the thickness of the surrounding rock loosened zone and reveal the influence of its characteristics. This study provides timely scientific reference for evaluating surrounding rock support and its effectiveness, meeting the demands of engineering practice.

Thickness of loosen zone  /  Particle swarm optimization  /  Stacked ensemble learning  /  Machine learning  /  SHAP
张全明, 刘伟, 唐敬晓, 刘浩, 翟珂. 基于可解释性机器学习的巷道围岩松动圈厚度智能预测. 矿业研究与开发, 2025 , 45 (12) : 106 -114 .
Quanming ZHANG, Wei LIU, Jingxiao TANG, Hao LIU, Ke ZHAI. Intelligent Prediction of Thickness of the Surround Rock Loosened Zone in Roadways Based on Interpretable Machine Learning[J]. Mining Research and Development, 2025 , 45 (12) : 106 -114 .
Year 2025 volume 45 Issue 12
107
50
Cite this Article
BibTeX
Article Info
  • Receive Date:2024-12-26
  • Online Date:2026-10-28
  • Published:2025-12-25
Article Data
Affiliations
History
  • Received:2024-12-26
Funding
Affiliations
    1.Yanzhou Mining Group Co., Ltd., Xinglongzhuang Coal Mine, Jining, Shandong 272102, China
    2.Laboratory of Mine Disaster Prevention and Control, Shandong University of Science and Technology, Taian, Shandong 271019, China
References
Share
https://castjournals.cast.org.cn/joweb/kyyjykf/EN/
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT