A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability
To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6656882 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546906210828288 |
|---|---|
| author | Jinglai Sun Darui Ren Yu Song Mingyuan Yu Zhaofei Chu Baoguo Liu Shaogang Li Xinyang Guo |
| author_facet | Jinglai Sun Darui Ren Yu Song Mingyuan Yu Zhaofei Chu Baoguo Liu Shaogang Li Xinyang Guo |
| author_sort | Jinglai Sun |
| collection | DOAJ |
| description | To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering. |
| format | Article |
| id | doaj-art-d25ef244349b4b07ac68a4ea53ac25c4 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d25ef244349b4b07ac68a4ea53ac25c42025-02-03T06:46:43ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66568826656882A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock StabilityJinglai Sun0Darui Ren1Yu Song2Mingyuan Yu3Zhaofei Chu4Baoguo Liu5Shaogang Li6Xinyang Guo7Beijing Municipal Engineering Research Institute, Beijing 100037, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Civil Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaMine Safety Technology Branch, China Coal Research Institute, Beijing 100013, ChinaBGI Engineering Consultants LTD, Beijing 100085, ChinaTo accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering.http://dx.doi.org/10.1155/2021/6656882 |
| spellingShingle | Jinglai Sun Darui Ren Yu Song Mingyuan Yu Zhaofei Chu Baoguo Liu Shaogang Li Xinyang Guo A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability Shock and Vibration |
| title | A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability |
| title_full | A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability |
| title_fullStr | A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability |
| title_full_unstemmed | A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability |
| title_short | A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability |
| title_sort | novel model using virtual state variables and bayesian discriminant analysis to classify surrounding rock stability |
| url | http://dx.doi.org/10.1155/2021/6656882 |
| work_keys_str_mv | AT jinglaisun anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT daruiren anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT yusong anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT mingyuanyu anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT zhaofeichu anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT baoguoliu anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT shaogangli anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT xinyangguo anovelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT jinglaisun novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT daruiren novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT yusong novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT mingyuanyu novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT zhaofeichu novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT baoguoliu novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT shaogangli novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability AT xinyangguo novelmodelusingvirtualstatevariablesandbayesiandiscriminantanalysistoclassifysurroundingrockstability |