five

Damage evaluation of gypsum-containing rocks during bending using acoustic methods

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://data.mendeley.com/datasets/bpf7rz2kd4
下载链接
链接失效反馈
官方服务:
资源简介:
The article describes the regression obtained for the non-destructive assessment of the damage parameter ω of gypsum-containing rocks using the measured velocities of elastic waves Cp, Cs1, Cs2 and the acoustic quality factor Q. The experiments were carried out on beam specimens of gypsum-containing rocks of the Novomoskovsk field, Tula region, Russia, with bending by a three-point scheme. This coincided with the rock stress state in the roof of underground mining excavations. In the upper part of the sample, there was a layer of dolomite, which had a higher strength, lower acoustic losses and a higher acoustic quality factor compared to gypsum in the lower part of the sample. A low cycle fatigue regime was established to reduce the study time. The experiment was carried out in a series of 100 load/unload cycles. The velocities of the longitudinal and transverse (along and across the direction of loading) elastic waves, as well as the acoustic Q factor, were measured before and between cycles. The maximum load of the cycle in each subsequent series was increased in comparison with the previous series to find the mode of low cycle fatigue. The damage parameter was estimated as ωi=ΣNi/Nf, where ωi is the current value of the damage parameter after each i-th series of loads, ΣNi is the total number of AE events from the beginning of the experiment and Nf is the total number of AE events at destruction, Nf = 5226 imp. Regression dependencies and its accuracy estimations give the best result by the combination of all parameters, such as the velocities of longitudinal and two transverse elastic waves, the acoustic quality factor and its derivative on number of cycles. The error was almost five times smaller than for the velocity of only the longitudinal waves. The study was supported by the Russian Foundation for Basic Research, grant No. 17-05-00570.
创建时间:
2019-12-29
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作