Characterizing Failure Mechanism of Soft and Hard Rocks: Implication from Acoustic Emission and Machine Learning

At the laboratory scale, differentiating damage evolution mechanisms between soft and hard rocks and objectively identifying stress states are prerequisites for uncovering rock instability failure mechanisms. To comparatively analyze the differences in the failure processes of soft and hard rocks, this study systematically investigated the fracture evolution characteristics of samples of both rock types using acoustic emission (AE) parameters, and developed a machine learning-driven rock fracture state classification model. The results indicate that: soft and hard rocks exhibit significantly distinct phased characteristics in AE frequency, energy, and peak frequency across stress stages; the evolution process of internal microcracks in rocks was revealed through AE event location, average frequency (AF) and rise angle (RA) information; the evolution patterns of b-value and information entropy across “counts-spatial-energy” dimensions were analyzed, clarifying distinct failure mechanisms between soft rocks’ progressive plastic deformation and hard rocks’ abrupt brittle failure; the stress stages of soft and hard rocks were classified based on AE multi-parameters using machine learning classification methods, and the classification model showed high accuracy while quantitatively ranking feature importance. These findings not only effectively identify differences in fracture mechanisms between soft and hard rocks at the laboratory scale, but also highlight the potential of machine learning models for processing AE time-series data.

Recommended citation: Li, Zhuang; Zhang, Zeqiang; Xu, Nuwen; Gao, Feng; Li, Biao. (2026). "Characterizing Failure Mechanism of Soft and Hard Rocks: Implication from Acoustic Emission and Machine Learning." Rock Mechanics and Rock Engineering.
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