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<p>Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering. This study presents a novel unsupervised classification framework based on Gaussian mixture modeling (GMM) for distinguishing acoustic emission (AE) signatures associated with different fracture modes in sandstone samples that contain prefabricated fissures at varying inclination angles. The frequency-domain characteristics of the AE signals were extracted using fast Fourier transform (FFT), while the RA–AF (rise time/amplitude versus average frequency) parameter space was employed to characterize the crack mechanisms. To increase classification accuracy and model robustness, the Bayesian information criterion (BIC) was introduced to determine the optimal number of Gaussian components. Experimental results from uniaxial compression tests reveal that fissure inclination significantly affects crack evolution behavior: low-angle fissures favor shear and hybrid cracks, whereas high-angle fissures cause tensile failure. The proposed GMM-based method effectively identifies tensile, shear, and hybrid cracks with increased objectivity and accuracy, outperforming traditional empirical RA–AF thresholding techniques. This research provides a reliable and generalizable approach for AE signal classification, which presents theoretical insights and practical support for real-time monitoring, early warning, and structural health assessment in fractured rock masses.</p>

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Crack type classification of fissured sandstone using acoustic emission and gaussian mixture modeling: Effects of fissure inclination angles

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Kang-sheng Xue,
  • Hai Pu,
  • Yin-long Lu,
  • Yan-long Chen,
  • Yu Wu,
  • Ming Li,
  • De-jun Liu,
  • Jun-ce Xu

摘要

Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering. This study presents a novel unsupervised classification framework based on Gaussian mixture modeling (GMM) for distinguishing acoustic emission (AE) signatures associated with different fracture modes in sandstone samples that contain prefabricated fissures at varying inclination angles. The frequency-domain characteristics of the AE signals were extracted using fast Fourier transform (FFT), while the RA–AF (rise time/amplitude versus average frequency) parameter space was employed to characterize the crack mechanisms. To increase classification accuracy and model robustness, the Bayesian information criterion (BIC) was introduced to determine the optimal number of Gaussian components. Experimental results from uniaxial compression tests reveal that fissure inclination significantly affects crack evolution behavior: low-angle fissures favor shear and hybrid cracks, whereas high-angle fissures cause tensile failure. The proposed GMM-based method effectively identifies tensile, shear, and hybrid cracks with increased objectivity and accuracy, outperforming traditional empirical RA–AF thresholding techniques. This research provides a reliable and generalizable approach for AE signal classification, which presents theoretical insights and practical support for real-time monitoring, early warning, and structural health assessment in fractured rock masses.