Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application
文献类型：期刊论文
| 作者 | Yin, Xin1,2; Liu, Quansheng1,2; Huang, Xing3; Pan, Yucong1,2 |
| 刊名 | TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
 |
| 出版日期 | 2021-11-01 |
| 卷号 | 117页码:21 |
| ISSN号 | 0886-7798 |
| 关键词 | Rockburst prediction Microseismic data Convolutional neural network Adaptive moment estimation optimization Bayes optimization |
| DOI | 10.1016/j.tust.2021.104133 |
| 英文摘要 | Rockburst is a dynamic geological disaster common during underground excavation, which significantly threatens the safety of personnel, equipment, and property. This paper proposes an integrated CNN-Adam-BO algorithm based on microseismic monitoring data to achieve real-time prediction of rockburst intensity. A total of 400 rockburst cases were collected from a water conveyance tunnel in northwestern China, including 100 no rockbursts, 252 low rockbursts, and 48 moderate rockbursts. During modeling, adaptive moment estimation optimization and Bayes optimization were separately used to tune the CNN network hyper-parameters (i.e., convolution kernels and bias) as well as dropout probability, and a parametric analysis was carried out to optimize the CNN structure and training parameters (i.e., the CNN architecture, the number of kernels in the convolutional layer, the number of neurons in the full connection layer, and the number of training epochs). To assess model performance, 2 global metrics (i.e., accuracy and Kappa) and 3 within-class metrics (i.e., precision, recall, and F1-score) were introduced. Additionally, a comparison analysis with other machine learning models, such as a back-propagation neural network, a support vector machine, and two CNN-based benchmark models, was conducted in regard to prediction ability and running speed. Results show that the CNN-Adam-BO model is the most superior, which achieved an accuracy of 91.67%, Kappa of 0.8392, F1-score of 0.8750, 0.9474, and 0.8333 (on no/low/moderate rockburst), and running time of 190.12 s. Taking 2 extra rockburst cases as examples, a complete workflow for how to apply the constructed model to construct a field rockburst risk warning system was illustrated. Further, the effect of data quality, especially data imbalance, on model performance was discussed, indicating that the synthetic minority oversampling technique is an effective way to eliminate data imbalance. Finally, superiority of the dimension enhancement method of the microseismic sequence described in this paper was verified via comparison with the continuous wavelet transform and cross wavelet transform. |
| 资助项目 | National Natural Science Foundation of China[41941018] ; National Natural Science Foundation of China[52074258] ; National Natural Science Foundation of China[41807250] ; China Postdoctoral Science Foundation Program[2019T120686] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:000696809200006 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/27915]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Liu, Quansheng; Huang, Xing; Pan, Yucong |
| 作者单位 | 1.Wuhan Univ, Sch Civil Engn, Key Lab Geotech & Struct Engn Safety Hubei Prov, Wuhan 430072, Peoples R China 2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Xin,Liu, Quansheng,Huang, Xing,et al. Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2021,117:21. |
| APA | Yin, Xin,Liu, Quansheng,Huang, Xing,&Pan, Yucong.(2021).Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,117,21. |
| MLA | Yin, Xin,et al."Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 117(2021):21. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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