Probability Analysis of Deep Tunnels Based on Monte Carlo Simulation: Case Study of Diversion Tunnels at Jinping II Hydropower Station, Southwest China
文献类型：期刊论文
| 作者 | Tu, Hongliang; Zhou, Hui; Gao, Yang; Lu, Jingjing; Singh, Hemant Kumar; Zhang, Chuanqing; Hu, Dawei; Hu, Mingming |
| 刊名 | INTERNATIONAL JOURNAL OF GEOMECHANICS
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 21期号:12页码:14 |
| ISSN号 | 1532-3641 |
| 关键词 | Jinping II hydropower station Deep-buried diversion tunnel Field monitoring Probability analysis Monte Carlo method |
| DOI | 10.1061/(ASCE)GM.1943-5622.0002146 |
| 英文摘要 | During the construction of deep-buried diversion tunnels, many uncertainties exist in the variables governing the safety of the support structure, which will affect the safety of the tunnel. Therefore, the probability analysis of the deep tunnel based on Monte Carlo simulation is analyzed. For that a case study of diversion tunnels at Jinping II hydropower station, southwestern China is selected and analyzed. First, the dispersions of geostress and marble mechanical parameters are studied based on the field monitoring of the bolt stress and the laboratory testing of the Jinping marble. Subsequently, the statistical characteristics of the radial subgrade modulus are obtained considering the 100,000-fold sampling. Finally, the influence of the main uncertain factors on the internal force and deformation of the supporting structure is examined through the establishment of a numerical model. The maximum bending moment and axial force appear in the vault and invert of the tunnel, respectively. When the design requires the reliability of the tunnel support structure to achieve 90% in the case of Jinping II hydropower station, the value of the bending moment and the axial force cannot be higher than 0.57 MPa and 8.0 MN, respectively. The factors those having a greater impact on the safety of the tunnel support structure are the vertical load and the radial subgrade modulus. |
| 资助项目 | National Key R&D Program of China[2018YFC0809601] ; National Key R&D Program of China[2019YFC0605103] ; National Key R&D Program of China[2019YFC0605104] ; National Natural Science Foundation of China (NSFC)[41941018] ; Key Projects of the Yalong River Joint Fund of the National Natural Science Foundation of China[U1865203] ; Hubei Province Natural Science Foundation innovation group[2018CFA013] ; China Postdoctoral Science Foundation[2019M662754] ; 2018 Hubei Province Postdoctoral Science and Technology Project[G123] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | ASCE-AMER SOC CIVIL ENGINEERS |
| WOS记录号 | WOS:000708123300011 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/28490]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Zhou, Hui |
| 作者单位 | 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 | Tu, Hongliang,Zhou, Hui,Gao, Yang,et al. Probability Analysis of Deep Tunnels Based on Monte Carlo Simulation: Case Study of Diversion Tunnels at Jinping II Hydropower Station, Southwest China[J]. INTERNATIONAL JOURNAL OF GEOMECHANICS,2021,21(12):14. |
| APA | Tu, Hongliang.,Zhou, Hui.,Gao, Yang.,Lu, Jingjing.,Singh, Hemant Kumar.,...&Hu, Mingming.(2021).Probability Analysis of Deep Tunnels Based on Monte Carlo Simulation: Case Study of Diversion Tunnels at Jinping II Hydropower Station, Southwest China.INTERNATIONAL JOURNAL OF GEOMECHANICS,21(12),14. |
| MLA | Tu, Hongliang,et al."Probability Analysis of Deep Tunnels Based on Monte Carlo Simulation: Case Study of Diversion Tunnels at Jinping II Hydropower Station, Southwest China".INTERNATIONAL JOURNAL OF GEOMECHANICS 21.12(2021):14. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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