LONG Neng-zeng, REN Song, WU Fei, et al. Degradation of argillaceous sandstone and identification of acoustic emission evolution characteristics under acidic wet-dry cycles[J]. 2024, 45(9): 2653-2668.
LONG Neng-zeng, REN Song, WU Fei, et al. Degradation of argillaceous sandstone and identification of acoustic emission evolution characteristics under acidic wet-dry cycles[J]. 2024, 45(9): 2653-2668.DOI: 10.16285/j.rsm.2023.1586.
Thisstudy aims to investigate the effects of wet-dry cycles caused byrainfall-evaporation and acid rain erosion on the stability of argillaceoussandstone slopes. It examines the degradation patterns of mechanical parametersand the evolution of damage and failure in argillaceous sandstone subjected tovarious acidic wet-dry cycles using uniaxial and triaxial compression tests andacoustic emission monitoring tests. The results indicate a positive correlationbetween the degradations degrees of uniaxial compressive strength ()
elastic modulus ()
cohesion ()
and internal friction angle ()of argillaceous sandstoneand the number of wet-dry cycles. Conversely
the degradation rate shows anegative correlation with the cycles. Confining pressure retards thedegradation process
while acidic environments intensify the degradation ofrock samples with being highly sensitiveunder acidic wet-dry cycles. Mechanical parameters exhibit a significantlogarithmic relationship with the number of wet-dry cycles across different pHconditions. The acoustic emission ringing counts and energy evolutioncharacteristics can be categorized into three phases: an initial active phase
a stable growth phase
and a peak rapid increase phase. Distinct precursors toabrupt failure are evident during the stable growth phase. The RA-AF tensilecrack signals consistently decrease due to the strengthening effects of acidicwet-dry cycles. Additionally
the characteristics of acoustic emissionlocalization events shift from a central axial cluster to an oblique randomdistribution
indicating a transition in damage from tensile to shear failure.The composite accuracy for identifying the damage state of argillaceoussandstone using an acoustic emission data-driven model achieved 93.33%. Theringing count and maximum energy features contributed 81.63% to this accuracy
highlighting the model’s ability to accurately capture the intrinsiccorrelation between damage state levels and acoustic emission data.