Prediction and Evaluation of Rockburst Based on Depth Neural Network
The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8248443 |
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| _version_ | 1832561530011385856 |
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| author | Jin Zhang Mengxue Wang Chuanhao Xi |
| author_facet | Jin Zhang Mengxue Wang Chuanhao Xi |
| author_sort | Jin Zhang |
| collection | DOAJ |
| description | The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel. |
| format | Article |
| id | doaj-art-cc5efcf812104390881b896752988f85 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-cc5efcf812104390881b896752988f852025-02-03T01:24:47ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/82484438248443Prediction and Evaluation of Rockburst Based on Depth Neural NetworkJin Zhang0Mengxue Wang1Chuanhao Xi2College of Civil Engineering, Qingdao University of Technology, Qingdao 26033, ChinaCollege of Civil Engineering, Qingdao University of Technology, Qingdao 26033, ChinaCollege of Civil Engineering, Qingdao University of Technology, Qingdao 26033, ChinaThe formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.http://dx.doi.org/10.1155/2021/8248443 |
| spellingShingle | Jin Zhang Mengxue Wang Chuanhao Xi Prediction and Evaluation of Rockburst Based on Depth Neural Network Advances in Civil Engineering |
| title | Prediction and Evaluation of Rockburst Based on Depth Neural Network |
| title_full | Prediction and Evaluation of Rockburst Based on Depth Neural Network |
| title_fullStr | Prediction and Evaluation of Rockburst Based on Depth Neural Network |
| title_full_unstemmed | Prediction and Evaluation of Rockburst Based on Depth Neural Network |
| title_short | Prediction and Evaluation of Rockburst Based on Depth Neural Network |
| title_sort | prediction and evaluation of rockburst based on depth neural network |
| url | http://dx.doi.org/10.1155/2021/8248443 |
| work_keys_str_mv | AT jinzhang predictionandevaluationofrockburstbasedondepthneuralnetwork AT mengxuewang predictionandevaluationofrockburstbasedondepthneuralnetwork AT chuanhaoxi predictionandevaluationofrockburstbasedondepthneuralnetwork |