Experimental study on the evolutionary characteristics of acoustic signals produced by granite damage under uniaxial compression

Abstract

Acoustic signals emitted during rock fracture constitute an important tool for rock damage evaluation. To investigate the evolutionary characteristics of acoustic emission (AE), microseismic (MS), and sound signals produced by hard rock fracture, uniaxial compression tests on granite specimens observed by AE, MS and sound monitoring were carried out. The evolution characteristics of the acoustic signal index, including its waveform, fractal dimension, b value, main frequency, energy proportion of signal frequency bands, and signal activeness, were analysed. The results indicate that there are significant differences in some characteristics of the AE, MS, and sound on the eve of granite failures, such as the waveform amplitude density, the average decline rate of the b value, the distribution of the main frequency, and the evolution of the energy proportion of the advantage frequency band. The three types of acoustic signals can characterize different scales of rock fracture under uniaxial compression. AE is sensitive to small-scale rock fractures, and MS and sound are sensitive to large-scale rock fractures. In addition, a unified damage evolution equation established by acoustic signals is proposed to quantitatively describe the damage process of granite specimens during uniaxial compression tests.

Experimental study on the evolutionary characteristics of acoustic signals produced by granite under biaxial compression with different intermediate principal stresses

“…Additionally, the difference in the period when accelerated growth occurs can be clearly identified (see Figure 7), that is, AE < MS < sound in order of stress level from low to high. This phenomenon arises from differences in the frequency ranges, propagation and capture routes and acquisition modes among three types of acoustic signals, which can be evidenced by the following: AE signals, characterized by high frequency, propagation and capture in rock, as well as acquisition motivated by hit parameters; MS signals, characterized by low frequency, propagation and capture in rock, and utilizing waveform flowbased acquisition; sound signals, characterized by low frequency, propagation in rock and air and capture in air, and also employing waveform flow-based acquisition (Cai et al, 2007;Su et al, 2023;Zhao G et al, 2022). Furthermore, with the increase in the intermediate principal stress σ 2 , the initial stress of activeness in each of the signals decreases, as shown in Table 2.…”

“…Based on the experimental results, it can be concluded that in terms of the activeness, b value, main frequency and proportion of advantage frequency bands, AE, MS and sound signals exist in obvious phenomena preceding the failure of granite subjected to biaxial compression, and the similar phenomena in the three types of signals are summarized in Table 6. However, there are significant differences (see Table 7) in the precursors between AE, MS and sound signals caused by the distinctive nature of acoustic signals (e.g., frequency ranges, acquisition modes and sampling rates) (Su et al, 2021;Su et al, 2023;Zhao G et al, 2022).…”

“…Sun et al (2021) found that MS event cluster characteristics based on the moment tensor inversion method can detect the occurrence of the rupture mode (dominated by tensile fractures) of a dome roof in a main transformer room. However, a study of the evolutionary characteristics of sound signals produced by hard rocks under biaxial compression has not been reported; moreover, there is a gap in the research about the similarities and differences in the evolutionary characteristics of AE, MS and sound signals under the biaxial compression of hard rocks [except for uniaxial compression (Su et al, 2023)], which can contribute to the integrated usage of three types of signals in the prediction and warning of engineering geological disasters.…”

A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals

“…To accurately obtain abundant AE signals, the AE sensor (e.g., 1 sensor in Brazilian disc test and 2 sensors in direct shear test) was arranged as the Figures 3C, D. With reference to some studies of the rock mechanics studies using AE monitoring, the parameters of the AE system were set as shown in Table 1 (Su et al, 2020;Su et al, 2023).…”

Damage evolution analysis of concrete based on multi-feature acoustic emission and Gaussian mixture model clustering

“…At present, AE signal analysis methods can be generally categorized into two approaches: parameter-based and waveform-based analyses (Yang et al., 2021). Traditional approaches, known for their simplicity and high efficiency, are suitable for the qualitative assessment of crack development and damage evolution (Su et al., 2023). Researchers have employed various AE parameters to characterize the damage evolution process in CFRP-strengthened and reinforced concrete structures, including amplitude, energy, count, peak frequency (Nair et al., 2020; Zhang and Li, 2020).…”