Coal–Rock Catastrophic Collapse: Precursors Based on AE and Fiber Bundle Models

Abstract

This paper proposes a new precursor for monitoring coal–rock dynamic disasters based on a fiber bundle model (FBM), which has been validated in the study of material fracture and critical phenomena. First, the FBM was simulated using the Monte Carlo method to analyze the variations of force and energy. The derivative of energy was identified as a precursor characteristic for model failure. The acoustic emission (AE) features of coal–rock under uniaxial compression were also analyzed, and a constitutive model for coal–rock damage evolution under uniaxial compression was established using AE ringing count. Furthermore, the energy derivative was calculated using the constitutive model to verify the simulation results and propose a new precursor indicator for coal–rock collapse. The research results provide useful guidance for preventing coal mine dynamic disasters. This study presents a novel methodology for predicting engineering geological hazards, focusing specifically on monitoring and preventing rockburst disasters in coal mines. The crucial precursor characteristics of coal and rock damage are unveiled by research findings, offering valuable insights for the accurate forecasting of potential disaster risks. This approach holds substantial promise not only within the coal mining sector but also across various engineering domains, including geotechnical engineering and other fields necessitating meticulous risk assessment. Implementation of this methodology empowers practitioners to refine disaster prediction, proactively ensuring the sustainability and safety of engineering ventures. It is recommended that project teams consider the integration of this innovative indicator alongside widely adopted microseismic monitoring techniques, thus mitigating the limitations of existing geophysical monitoring methods. This innovative approach is poised to have a profound and transformative impact on the enhancement of geological hazard monitoring and engineering risk management, providing invaluable support for upcoming engineering projects.