Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine
Rock burst disasters occurring in underground high-stress rock mass mining and excavation engineering seriously threaten the safety of workers and hinders the progress of engineering construction. Rock burst classification prediction is the basis of reducing and even eliminating rock burst hazards....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/18/3276 |
| _version_ | 1797485201983537152 |
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| author | Shaohong Yan Yanbo Zhang Xiangxin Liu Runze Liu |
| author_facet | Shaohong Yan Yanbo Zhang Xiangxin Liu Runze Liu |
| author_sort | Shaohong Yan |
| collection | DOAJ |
| description | Rock burst disasters occurring in underground high-stress rock mass mining and excavation engineering seriously threaten the safety of workers and hinders the progress of engineering construction. Rock burst classification prediction is the basis of reducing and even eliminating rock burst hazards. Currently, most of mainstream discriminant models for rock burst grade prediction are based on small samples. Comprehensive selection according to many pieces of literature, the maximum tangential stress of surrounding rock and rock uniaxial compressive strength ratio coefficient (stress state parameter), rock uniaxial compressive strength and uniaxial tensile strength ratio (brittleness modulus), and the elastic energy index are used as a grading evaluation index of rock burst based on the collection of different construction engineering instances of rock burst in 114 groups of extensive sample data in different regions of the world, which are used to carry out the training study. The representativeness and accuracy of the index selection were verified by the indicator variance analysis and Spearman correlation coefficient hypothesis test. The Intelligent Rock burst Identification System (IRIS) based on an optimizable SVM model was established using data set samples. After extensive data cross-validation training, the accuracy of the SVM discriminant analysis model can reach 95.6%, which is significantly better than the prediction accuracy of the traditional SVM model of 71.9%. The model is used to classify and predict the rock burst intensity of 10 typical projects at home and abroad. The prediction results are consistent with the actual rock burst intensity, which is better than the discriminant model based on small sample data and other existing prediction models. The application of engineering examples shows that the results of the rock burst intensity classification prediction model based on extensive sample data processing analysis and the SVM discriminant method are in good agreement with the actual rock burst intensity, which can effectively provide a reference for the prediction of rock burst intensity grade in a construction area. |
| first_indexed | 2024-03-09T23:15:22Z |
| format | Article |
| id | doaj.art-302778a4354d4e49a4f8fbe0f9e491e4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:15:22Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-302778a4354d4e49a4f8fbe0f9e491e42023-11-23T17:35:47ZengMDPI AGMathematics2227-73902022-09-011018327610.3390/math10183276Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector MachineShaohong Yan0Yanbo Zhang1Xiangxin Liu2Runze Liu3College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Science, North China University of Science and Technology, Tangshan 063210, ChinaRock burst disasters occurring in underground high-stress rock mass mining and excavation engineering seriously threaten the safety of workers and hinders the progress of engineering construction. Rock burst classification prediction is the basis of reducing and even eliminating rock burst hazards. Currently, most of mainstream discriminant models for rock burst grade prediction are based on small samples. Comprehensive selection according to many pieces of literature, the maximum tangential stress of surrounding rock and rock uniaxial compressive strength ratio coefficient (stress state parameter), rock uniaxial compressive strength and uniaxial tensile strength ratio (brittleness modulus), and the elastic energy index are used as a grading evaluation index of rock burst based on the collection of different construction engineering instances of rock burst in 114 groups of extensive sample data in different regions of the world, which are used to carry out the training study. The representativeness and accuracy of the index selection were verified by the indicator variance analysis and Spearman correlation coefficient hypothesis test. The Intelligent Rock burst Identification System (IRIS) based on an optimizable SVM model was established using data set samples. After extensive data cross-validation training, the accuracy of the SVM discriminant analysis model can reach 95.6%, which is significantly better than the prediction accuracy of the traditional SVM model of 71.9%. The model is used to classify and predict the rock burst intensity of 10 typical projects at home and abroad. The prediction results are consistent with the actual rock burst intensity, which is better than the discriminant model based on small sample data and other existing prediction models. The application of engineering examples shows that the results of the rock burst intensity classification prediction model based on extensive sample data processing analysis and the SVM discriminant method are in good agreement with the actual rock burst intensity, which can effectively provide a reference for the prediction of rock burst intensity grade in a construction area.https://www.mdpi.com/2227-7390/10/18/3276rock burstrock mechanicshierarchical predictionanalysis of variancelarge sample dataSpearman correlation coefficient hypothesis test |
| spellingShingle | Shaohong Yan Yanbo Zhang Xiangxin Liu Runze Liu Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine Mathematics rock burst rock mechanics hierarchical prediction analysis of variance large sample data Spearman correlation coefficient hypothesis test |
| title | Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine |
| title_full | Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine |
| title_fullStr | Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine |
| title_full_unstemmed | Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine |
| title_short | Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine |
| title_sort | rock burst intensity classification prediction model based on a bayesian hyperparameter optimization support vector machine |
| topic | rock burst rock mechanics hierarchical prediction analysis of variance large sample data Spearman correlation coefficient hypothesis test |
| url | https://www.mdpi.com/2227-7390/10/18/3276 |
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