Coal bursting liability determination by needle penetration test: Empirical criterion and machine learning
Abstract Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst. The bursting liability is important to evaluate coal burst in mining. In this paper, the needle penetration test was carried out to determinate the coal bursting liability, and the empiric...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-12-01
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| Series: | International Journal of Coal Science & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40789-024-00738-1 |
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| Summary: | Abstract Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst. The bursting liability is important to evaluate coal burst in mining. In this paper, the needle penetration test was carried out to determinate the coal bursting liability, and the empirical criterion of coal bursting liability was proposed. Moreover, the machine learning method was applied to coal bursting liability determination. Through analyzing the elastic strain energy release and failure time, the residual elastic strain energy release rate index K RE was proposed to evaluate the coal bursting liability. According to the relationship between needle penetration index (NPI), K RE and the critical value of K RE, the Needle Penetration Test-based Empirical Classification Criterion (NPT-ECC) was obtained. In addition, four machine learning classification models were constructed. After training and testing of the models, Needle Penetration Test-based Machine Learning Classification Model (NPT-MLCM) was proposed. The research results show that the accuracy of NPT-ECC is 6.66% higher than that of China National Standard Comprehensive Evaluation (CNSCE) according to verification of the coal fragment ejection ratio F. Gridsearch cross validation-extreme gradient boosting (GSCV-XGBoost) has the best prediction performance among all the models, and accuracy, Macro-Precision, Macro-Recall and Macro-F1-score of which were 86.67%, 88.97%, 87.50% and 87.37%. Based on this, the Needle Penetration Test-based GSCV-XGBoost (NPT-GSCV-XGBoost) was proposed. After comparative analysis and discussion, NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability. |
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| ISSN: | 2095-8293 2198-7823 |