Crack propagation characteristics during progressive failure of circular tunnels and the early warning thereof based on multi-sensor data fusion

被引:51
作者
Zhang, Liming [1 ,2 ]
Chao, Wenwen [3 ]
Liu, Zhongyuan [3 ]
Cong, Yu [1 ,2 ]
Wang, Zaiquan [1 ,2 ]
机构
[1] Qingdao Univ Technol, Sch Civil Engn, Qingdao 266033, Peoples R China
[2] Qingdao Univ Technol, Cooperat Innovat Ctr Engn Construct & Safety Shan, Qingdao 266033, Peoples R China
[3] Qingdao Univ Technol, Sch Sci, Qingdao 266033, Peoples R China
基金
中国国家自然科学基金;
关键词
Tunnel; Crack propagation; Acoustic emission; Failure warning; Multi-sensor data fusion; ACOUSTIC-EMISSION; ROCK; ROCKBURST; PRECURSOR; FRACTURE;
D O I
10.1007/s40948-022-00482-3
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Biaxial compression tests were conducted on circular tunnels that constructed in sandstone to reveal the evolution of macro-cracks, the strain field, and acoustic emission (AE) parameters during deformation of tunnels. An early-warning method for tunnel failure based on multi-sensor data fusion was studied. The results indicate that tensile cracks first initiate at the arch haunch of the tunnel; then shear cracks occur on both sides of the opening, forming two asymmetric V-shaped grooves; finally, tensile-shear cracks on both sides of the tunnel propagate to the vault, triggering brittle failure of the tunnel. The strain concentration zones appear earlier than cracks in the tunnel, and the location and range of strain concentration zones cover the crack-propagation paths. Before the failure of tunnels, a series of precursor events occur, including an AE quiet period, a significant, rapid drop in the AE-based b value, and abrupt increases in AE entropy and covariance of the strain field. The monitoring data of each physical field provide early warning asynchronously: the early-warning time of AE entropy is the earliest while that of the AE quiet period is the latest. Membership functions are constructed taking early-warning results of monitoring data in various physical cases as bodies of evidence, and their critical values are determined using normal distribution. Multi-sensor data are fused using the Dempster-Shafer (D-S) evidence theory to realize time-varying prediction and provision of early warning with graded probabilities for tunnel failure based on collaborative multi-sensor data fusion.
引用
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页数:24
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