Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion
文献类型：期刊论文
| 作者 | Yin, Xin3; Cheng, Shouye1,4; Yu, Honggan5; Pan, Yucong5; Liu, Quansheng5; Huang, Xing2; Gao, Feng1,4; Jing, Guoye1,4 |
| 刊名 | TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 152页码:22 |
| 关键词 | TBMs Rockburst Early warning Multi-source data fusion Probabilistic ensemble learning |
| ISSN号 | 0886-7798 |
| DOI | 10.1016/j.tust.2024.105915 |
| 英文摘要 | In recent years, tunnel boring machines (TBMs) have become extensively utilized in underground engineering projects. However, as a prevalent type of dynamic geological hazard, rockburst poses a serious threat to the safety of personnel, equipment, and property. To ensure the safe advancement of TBMs, we integrate the data of microseismic monitoring, TBM excavation, and surrounding rock and establish multiple probabilistic assessment models of rockburst risk using probabilistic ensemble learning and quantum particle swarm optimization. In order to identify the most optimal model for geological engineers, a comprehensive evaluation system is devised based on metrics like accuracy, Cohen's kappa, and F1-score. This system provides a thorough assessment of the models' global and local generalization performance. The results indicate that the classification and regression tree-extremely randomized trees (CART-ERT) hybrid model achieves the highest score, demonstrating superior generalization performance with an accuracy of 93.06% and a Cohen's kappa of 0.9074. Moreover, the F1-scores for no rockburst, low rockburst, moderate rockburst, and strong rockburst are 0.9412, 0.9444, 0.9189, and 0.9189, respectively. Based on the operational framework of the CART-ERT hybrid model, a user-friendly graphical user interface (GUI) system is developed. This significantly enhances the practicality and deployability of the model. Through application in a TBM diversion tunnel project in Xinjiang, the on-site early-warning accuracy of rockburst with the GUI system reaches 90%. Notably, the GUI system maintains high early-warning sensitivity and reliability for high-intensity rockburst such as moderate and strong ones. Lastly, to enhance the model's interpretability, a variable importance measure (VIM) analysis on 14 features extracted from three types of heterogeneous data is conducted to assess their contributions to the early warning of rockburst. We discover that the cumulative energy change rate of microseismic events exhibits the highest importance score, indicating its crucial role in rockburst early warning. |
| 资助项目 | National Natural Science Foundation of China[41941018] ; National Natural Science Foundation of China[42177140] ; Key Research and Development Project of Hubei Province[2021BCA133] ; Young Elite Scientist Sponsorship Program by CAST[YESS20230742] ; Special Funds for Technological Innovation and Entrepreneurship of Tiandi Science and Technology Company Limited[2021-2-TD-ZD006] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001345266200001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/43044]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Yu, Honggan |
| 作者单位 | 1.Tiandi Sci & Techol Co Ltd, Res Inst Mine Construct, Beijing 100013, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.City Univ Hong Kong, Dept Architecture & Civil Engn, Tat Chee Ave, Hong Kong, Peoples R China 4.State Key Lab Intelligent Coal Min & Strata Contro, Beijing 100013, Peoples R China 5.Wuhan Univ, Sch Civil Engn, Wuhan 430072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Xin,Cheng, Shouye,Yu, Honggan,et al. Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2024,152:22. |
| APA | Yin, Xin.,Cheng, Shouye.,Yu, Honggan.,Pan, Yucong.,Liu, Quansheng.,...&Jing, Guoye.(2024).Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,152,22. |
| MLA | Yin, Xin,et al."Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 152(2024):22. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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