Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces
The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775524002890 |
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| author | Wenhao Yi Mingnian Wang Qinyong Xia Yongyi He Hongqiang Sun |
| author_facet | Wenhao Yi Mingnian Wang Qinyong Xia Yongyi He Hongqiang Sun |
| author_sort | Wenhao Yi |
| collection | DOAJ |
| description | The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests. |
| format | Article |
| id | doaj-art-98d72670c6df4b308ba558eec2d76162 |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-98d72670c6df4b308ba558eec2d761622025-01-17T04:49:10ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552025-01-01171258274Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel facesWenhao Yi0Mingnian Wang1Qinyong Xia2Yongyi He3Hongqiang Sun4School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, ChinaSchool of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, ChinaSchool of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, ChinaSchool of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, China; China Railway Third Bureau Group Co., Ltd., Taiyuan, 030001, China; Corresponding author. School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China; Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, ChinaThe suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.http://www.sciencedirect.com/science/article/pii/S1674775524002890Rockburst predictionDrilling parametersFeature systemExtreme tree (ET)Bayesian optimization |
| spellingShingle | Wenhao Yi Mingnian Wang Qinyong Xia Yongyi He Hongqiang Sun Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces Journal of Rock Mechanics and Geotechnical Engineering Rockburst prediction Drilling parameters Feature system Extreme tree (ET) Bayesian optimization |
| title | Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces |
| title_full | Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces |
| title_fullStr | Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces |
| title_full_unstemmed | Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces |
| title_short | Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces |
| title_sort | rockburst prediction based on multi featured drilling parameters and extreme tree algorithm for full section excavated tunnel faces |
| topic | Rockburst prediction Drilling parameters Feature system Extreme tree (ET) Bayesian optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1674775524002890 |
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