Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network
A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective w...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/3453614 |
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| _version_ | 1832546147425583104 |
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| author | Yuchao Zheng Heng Zhong Yong Fang Wensheng Zhang Kai Liu Jing Fang |
| author_facet | Yuchao Zheng Heng Zhong Yong Fang Wensheng Zhang Kai Liu Jing Fang |
| author_sort | Yuchao Zheng |
| collection | DOAJ |
| description | A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction. |
| format | Article |
| id | doaj-art-967df9f30db24472a7869353048634dd |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-967df9f30db24472a7869353048634dd2025-02-03T07:23:49ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/34536143453614Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural NetworkYuchao Zheng0Heng Zhong1Yong Fang2Wensheng Zhang3Kai Liu4Jing Fang5Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaKey Laboratory of Transportation Tunnel Engineering, Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaKey Laboratory of Transportation Tunnel Engineering, Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaKey Laboratory of Transportation Tunnel Engineering, Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaKey Laboratory of Transportation Tunnel Engineering, Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaKey Laboratory of Transportation Tunnel Engineering, Ministry of Education, School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaA rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.http://dx.doi.org/10.1155/2019/3453614 |
| spellingShingle | Yuchao Zheng Heng Zhong Yong Fang Wensheng Zhang Kai Liu Jing Fang Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network Advances in Civil Engineering |
| title | Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network |
| title_full | Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network |
| title_fullStr | Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network |
| title_full_unstemmed | Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network |
| title_short | Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network |
| title_sort | rockburst prediction model based on entropy weight integrated with grey relational bp neural network |
| url | http://dx.doi.org/10.1155/2019/3453614 |
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