The Rock Burst Hazard Evaluation Using Statistical Learning Approaches
The great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control fa...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5576480 |
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| _version_ | 1850162083895705600 |
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| author | Jie Chen Jingkuan Gao Yuanyuan Pu Mingzhong Gao Like Wei Chong Wang Bo Peng Xusheng Zhao Guangchao Zhang Zhigang Zhang |
| author_facet | Jie Chen Jingkuan Gao Yuanyuan Pu Mingzhong Gao Like Wei Chong Wang Bo Peng Xusheng Zhao Guangchao Zhang Zhigang Zhang |
| author_sort | Jie Chen |
| collection | DOAJ |
| description | The great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. In this study, Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. The results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. The Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data. |
| format | Article |
| id | doaj-art-fd8b2301bdfc44f18c85be2594c43c36 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-fd8b2301bdfc44f18c85be2594c43c362025-08-20T02:22:39ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/55764805576480The Rock Burst Hazard Evaluation Using Statistical Learning ApproachesJie Chen0Jingkuan Gao1Yuanyuan Pu2Mingzhong Gao3Like Wei4Chong Wang5Bo Peng6Xusheng Zhao7Guangchao Zhang8Zhigang Zhang9State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, ChinaState Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, ChinaState Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, ChinaCollege of Water Resources and Hydropower, Sichuan University, Chengdu 610065, ChinaInformation Research Institute, Ministry of Emergency Management, Beijing 100029, ChinaInformation Research Institute, Ministry of Emergency Management, Beijing 100029, ChinaSichuan Coal Industry Group Limited Liability Company, Chengdu 610091, ChinaChina Coal Technology and Engineering Group Chongqing Research Institute, Chongqing 400037, ChinaCollege of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaState Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, ChinaThe great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. In this study, Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. The results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. The Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data.http://dx.doi.org/10.1155/2021/5576480 |
| spellingShingle | Jie Chen Jingkuan Gao Yuanyuan Pu Mingzhong Gao Like Wei Chong Wang Bo Peng Xusheng Zhao Guangchao Zhang Zhigang Zhang The Rock Burst Hazard Evaluation Using Statistical Learning Approaches Shock and Vibration |
| title | The Rock Burst Hazard Evaluation Using Statistical Learning Approaches |
| title_full | The Rock Burst Hazard Evaluation Using Statistical Learning Approaches |
| title_fullStr | The Rock Burst Hazard Evaluation Using Statistical Learning Approaches |
| title_full_unstemmed | The Rock Burst Hazard Evaluation Using Statistical Learning Approaches |
| title_short | The Rock Burst Hazard Evaluation Using Statistical Learning Approaches |
| title_sort | rock burst hazard evaluation using statistical learning approaches |
| url | http://dx.doi.org/10.1155/2021/5576480 |
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