Relationship between Local Deformation and Global Stability of Rock Using the Global-Local Dissipation Energy Time-Series Variation Coefficient

被引:0
|
作者
Lv, Xiangfeng [1 ,2 ]
Li, Xinyue [1 ]
Wang, Nianjin [3 ]
Ouyang, Wei [3 ]
Guo, Zhongmeng [3 ]
机构
[1] Univ Sci & Technol Beijing, Sch Civil & Resource Engn, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Beijing Key Lab Urban Underground Space Engn, Beijing 100083, Peoples R China
[3] Power China Rd Bridge Grp Co Ltd, Beijing 100032, Peoples R China
关键词
Local tension and slide; Global stability; Digital image correlation; Dissipative energy; Time-series variation coefficient; DIGITAL IMAGE CORRELATION; STRAIN LOCALIZATION; FAILURE; SANDSTONE; COMPRESSION; CRITERION; MECHANISM; EVOLUTION; GRANITE; TUNNELS;
D O I
10.1061/IJGNAI.GMENG-8242
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The tension and slide of surrounding rock are highly inclined to cause dynamic disasters, thus seriously threatening the safety of mine resource recovery. This study proposes a novel method based on the global-local dissipation energy time-series variation coefficient in order to analyze the relationship between local deformation and global stability of rock. The localized light-force monitoring test method is used to analyze the tensile-sliding effect, and the evolution characteristics of the global-local dissipative energy time-series variation coefficient are compared. The results indicate that the strength reduction of rock is affected by the deformation accumulation of the loading displacement field. As for the stable damage specimen, the sliding displacement occurs 5.5 s earlier than tensile displacement, which is 4.4 s longer than the case of the transient damage specimen. The node spring model theory is further proposed to determine whether the global-local dissipation energy relationship of rock under load conformed to the linear function distribution. The global-local dissipation energy time-series variation coefficient range is always in the [1, 0.9] range. Rock instability can be predicted based on the slope and inflection point changes in the time-series variation coefficient curve, and increasing the number of effective monitoring points can significantly improve the monitoring effect and accuracy of the local time-series variation coefficient. (c) 2023 American Society of Civil Engineers.
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页数:14
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