Multi-index fusion database and intelligent evaluation modelling for geostress classification
文献类型：期刊论文
| 作者 | Duan, Shuqian1,2; Zhao, Gengchen2; Jiang, Quan3; Xiong, Jiecheng2; Sun, Yuanda2,6; Kou, Yongyuan4,5; Qiu, Shili3 |
| 刊名 | TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 149页码:17 |
| 关键词 | Deep rock engineering Geostress classification Automatic machine learning Intelligent evaluation model |
| ISSN号 | 0886-7798 |
| DOI | 10.1016/j.tust.2024.105802 |
| 英文摘要 | Accurate evaluation of geostress levels is crucial for the investigation, design, and construction of underground engineering. However, existing methods for geostress classification are limited by their reliance on singular indexes, leading to discrepancies between the evaluation results and the actual observations in engineering. The discrepancies have resulted in casualties and economic losses. To address the above issues, 204 sets of geostress levels data were collected from numerous documents in the study. Six indexes related to geostress levels have been identified by analyzing the collected data and traditional criteria. Additionally, an orthogonal experimental design was carried out on the range-based indexes to obtain a multi-index fusion database. Furthermore, an intelligent evaluation model for the geostress levels was established in combination with the AutoGluon automatic machine learning framework. Subsequently, software has been developed from this model for ease of use in practical engineering. After on-site data verification, the proposed model demonstrated an accuracy of 95%, outperforming five traditional criteria, with the highest accuracy among them being 70%. The research could provide an effective basis for geostress classification, thereby enhancing the safety of on-site construction personnel. |
| 资助项目 | National Natural Science Foundation of China[52325905] ; National Natural Science Foundation of China[52008376] ; Natural Science Foundation of Henan Province[242300421057] ; Young Elite Scientists Sponsorship Program by CAST[2023QNRC001] ; China Postdoctoral Science Foundation[2023T160200] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001240342300001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/41600]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Xiong, Jiecheng |
| 作者单位 | 1.Henan Urban Planning Inst & Corp, Zhengzhou 450044, Peoples R China 2.Zhengzhou Univ, Sch Civil Engn, Zhengzhou 450001, Henan, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 4.Jinchuan Grp Co Ltd, Jinchang 737100, Gansu, Peoples R China 5.Univ Sci & Technol Beijing, Beijing 100083, Peoples R China 6.Shanghai Urban Construct City Operat Grp Co Ltd, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Shuqian,Zhao, Gengchen,Jiang, Quan,et al. Multi-index fusion database and intelligent evaluation modelling for geostress classification[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2024,149:17. |
| APA | Duan, Shuqian.,Zhao, Gengchen.,Jiang, Quan.,Xiong, Jiecheng.,Sun, Yuanda.,...&Qiu, Shili.(2024).Multi-index fusion database and intelligent evaluation modelling for geostress classification.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,149,17. |
| MLA | Duan, Shuqian,et al."Multi-index fusion database and intelligent evaluation modelling for geostress classification".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 149(2024):17. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。