Scientific Reports (Nov 2024)
Study on the failure and acoustic emission characteristics of coal under graded cyclic loading and unloading stress paths
Abstract
Abstract To study the influence of cyclic disturbance stress on the mechanical behavior of coal during mining, a gas containing coal fluid-solid coupling servo seepage experimental system was used to conduct experimental research on the acoustic emission (AE) characteristics of gas containing coal under two stress paths of graded cyclic loading and unloading. The AE characteristics of coal damage and failure processes under different cyclic stress paths were analyzed. The research results indicate that: (1) The overall characteristics of AE signals for both graded cyclic loading and unloading paths are basically the same. With increasing of the amount of graded cyclic loading or unloading, the AE count reaches its maximum value when reaching failure, and the cumulative ringing calculation of AE increases exponentially. (2) The AE signals under the graded cyclic loading or unloading path exhibit obvious zoning characteristics. In the low and medium stress regions, the AE signal basically satisfies the Kaiser effect, while reaching the high stress region before failure, the AE signal exhibits a significant Felicity effect. (3) The concentration coefficient of AE and the intensity coefficient of the Kaiser effect have been newly defined. They are used to quantitatively characterize the extent of the Kaiser effect of AEs under graded cyclic stress. It was found that as the variation of graded cyclic stress increases, the concentration coefficient and Kaiser effect intensity coefficient both show a decreasing trend. (4) Combining the AF and RA values of AE, it was found that the coal failure signals of the two stress paths were basically similar, that is, the overall failure was mainly tensile failure, and the signals of cyclic unloading tensile failure were significantly more than those of cyclic loading. The AE signal characteristics studied in this article are of great significance for predicting coal power disasters.
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