Due to micro-deformation, wide spatial distribution, and high randomness of instability, stability assessment and early warning of slope rock mass remain challenging. To identify highly sensitive and robust indicators of rock mass damage, we investigated time–frequency dynamic differences between unstable rock mass and bedrock during failure, focusing on micro-vibration characteristics. We further examined their correlation with structural plane constraint strength. Our results showed that the degree of structural plane damage in unstable rock mass is positively correlated with the amplitude ratio between rock mass and bedrock, and negatively correlated with the frequency ratio. By integrating these dynamic indicators with classification algorithms, a dynamics-PSO-SVM-based stability analysis method for unstable rock mass was proposed. This model enabled rapid classification of rock mass stability states based on dynamic indicators. Laboratory-scale similarity experiments analyzed the evolution of amplitude and frequency ratios during rock mass instability, revealing stage-specific patterns in unstable rock mass. The method achieved 100% classification accuracy between stable and unstable states. Moreover, it effectively reduced interference from complex environmental excitation and equipment temperature drift in vibration monitoring data, demonstrating strong noise immunity. These findings enhance the practical applicability of dynamic evaluation methods for assessing the stability of unstable rock mass.
Citation: Hongqiang LI, Chen ZHAO, Zinan ZOU, Song JIANG, Mowen XIE, Xueliang ZHANG. Stability assessment of falling-type unstable rock mass based on amplitude ratio and frequency ratio[J]. AIMS Environmental Science, 2026, 13(1): 159-178. doi: 10.3934/environsci.2026007
Due to micro-deformation, wide spatial distribution, and high randomness of instability, stability assessment and early warning of slope rock mass remain challenging. To identify highly sensitive and robust indicators of rock mass damage, we investigated time–frequency dynamic differences between unstable rock mass and bedrock during failure, focusing on micro-vibration characteristics. We further examined their correlation with structural plane constraint strength. Our results showed that the degree of structural plane damage in unstable rock mass is positively correlated with the amplitude ratio between rock mass and bedrock, and negatively correlated with the frequency ratio. By integrating these dynamic indicators with classification algorithms, a dynamics-PSO-SVM-based stability analysis method for unstable rock mass was proposed. This model enabled rapid classification of rock mass stability states based on dynamic indicators. Laboratory-scale similarity experiments analyzed the evolution of amplitude and frequency ratios during rock mass instability, revealing stage-specific patterns in unstable rock mass. The method achieved 100% classification accuracy between stable and unstable states. Moreover, it effectively reduced interference from complex environmental excitation and equipment temperature drift in vibration monitoring data, demonstrating strong noise immunity. These findings enhance the practical applicability of dynamic evaluation methods for assessing the stability of unstable rock mass.
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Hongqiang LI, Chen ZHAO, Zinan ZOU, Song JIANG, Mowen XIE, Xueliang ZHANG. Stability assessment of falling-type unstable rock mass based on amplitude ratio and frequency ratio[J]. AIMS Environmental Science, 2026, 13(1): 159-178. doi: 10.3934/environsci.2026007
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