Degradation of argillaceous sandstone and identification of acoustic emission evolution characteristics under acidic wet-dry cycles

This study aims to investigate the effects of wet-dry cycles caused by rainfall-evaporation and acid rain erosion on the stability of argillaceous sandstone slopes. It examines the degradation patterns of mechanical parameters and the evolution of damage and failure in argillaceous sandstone subjected to various acidic wet-dry cycles using uniaxial and triaxial compression tests and acoustic emission monitoring tests. The results indicate a positive correlation between the degradations degrees of uniaxial compressive strength (<i>σ</i>), elastic modulus (<i>E</i>), cohesion (<i>c</i>), and internal friction angle (<i>φ</i>) of argillaceous sandstone and the number of wet-dry cycles. Conversely, the degradation rate shows a negative correlation with the cycles. Confining pressure retards the degradation process, while acidic environments intensify the degradation of rock samples with <i>σ</i> being highly sensitive under acidic wet-dry cycles. Mechanical parameters exhibit a significant logarithmic relationship with the number of wet-dry cycles across different pH conditions. The acoustic emission ringing counts and energy evolution characteristics can be categorized into three phases: an initial active phase, a stable growth phase, and a peak rapid increase phase. Distinct precursors to abrupt failure are evident during the stable growth phase. The RA-AF tensile crack signals consistently decrease due to the strengthening effects of acidic wet-dry cycles. Additionally, the characteristics of acoustic emission localization events shift from a central axial cluster to an oblique random distribution, indicating a transition in damage from tensile to shear failure. The composite accuracy for identifying the damage state of argillaceous sandstone using an acoustic emission datadriven model achieved 93.33%. The ringing count and maximum energy features contributed 81.63% to this accuracy, highlighting the model’s ability to accurately capture the intrinsic correlation between damage state levels and acoustic emission data.