Alexandria Engineering Journal (May 2025)

Objective determination of initiation stress and damage stress in rock failure using acoustic emission and digital image correlation

  • Peng Liang,
  • Yanbo Zhang,
  • Xulong Yao,
  • Guangyuan Yu,
  • Qiang Han,
  • Junling Liu

DOI
https://doi.org/10.1016/j.aej.2025.02.077
Journal volume & issue
Vol. 121
pp. 156 – 166

Abstract

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Crack initiation stress and damage stress are two critical characteristic stresses in the rock failure process, and their precise determination is essential for a comprehensive understanding of the progressive failure mechanisms of rocks. In this study, a granite shear experiment was conducted using acoustic emission sensors for detecting shear and primary wave signals, while principal strain was measured using the digital image correlation (DIC) method. Three damage variables were established based on the cumulative counts of acoustic emission shear waves, primary waves, and principal strain, and their evolution patterns were analyzed. The research aimed to explore a method for determining rock characteristic stresses by integrating acoustic emission monitoring with the DIC method. The findings indicate that the damage variable based on the cumulative count of acoustic emission primary waves exhibits an S-shaped curve, which effectively describes the early stage of rock fracture development. The damage variable based on the cumulative count of acoustic emission shear waves and principal strain shows an exponential change, accurately depicting the accelerated development stage of rock fracture damage. The trends of these two variables are closely aligned, with a correlation coefficient of 0.99, indicating a strong positive correlation. Acoustic emission primary waves are suitable for identifying crack initiation stress, while acoustic emission shear waves can serve as an alternative to the DIC method for determining damage stress. Additionally, an accurate identification and determination method for rock initiation and damage stresses based on acoustic emission monitoring was proposed, utilizing the Kneedle algorithm to automatically detect inflection points and identify sudden increases in the curve. These results provide a novel approach for accurately determining rock characteristic stresses, offering broader prospects for engineering applications.

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