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The Rock Burst Hazard Evaluation Using Statistical Learning Approaches
- Source :
- Shock and Vibration, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- 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.
- Subjects :
- Hazard (logic)
Article Subject
Generalization
Computer science
QC1-999
Gaussian
0211 other engineering and technologies
02 engineering and technology
010502 geochemistry & geophysics
computer.software_genre
01 natural sciences
Rock burst
Bayes' theorem
Naive Bayes classifier
symbols.namesake
Rock mechanics
Robustness (computer science)
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Civil and Structural Engineering
Physics
Mechanical Engineering
Geotechnical Engineering and Engineering Geology
Condensed Matter Physics
Mechanics of Materials
symbols
Data mining
computer
Subjects
Details
- ISSN :
- 18759203 and 10709622
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Shock and Vibration
- Accession number :
- edsair.doi.dedup.....da89bbaf8240305743eddacd1b4ddc3b