Spatial-temporal response and precursor characteristics of tensile failure on disc coal samples of different sizes combining AE, EMR and DIC techniques

Abstract

Tensile fractures in deep coal and rock mass can readily trigger dynamic calamities like rock bursts, significantly impacting the safety of coal mining. To enhance our understanding of coal’s tensile failure mechanism, Brazilian splitting failure experiments were conducted on coal samples with prefabricated cracks of different sizes. Acoustic emission (AE), electromagnetic radiation (EMR) and digital image correlation (DIC) techniques were used to analyze the instability failure process and precursor characteristics of coal samples. The results showed that, for coal samples of the same thickness, the tensile strength and peak strain gradually decreased with the increase of coal sample size, whereas the elastic modulus increased gradually, highlighting more pronounced brittleness characteristics and revealing a noticeable size effect. The time-varying evolution of AE energy, EMR energy and VE (virtual extensometer) elongation during the tensile failure process of coal samples is closely correlated to the stress evolution trend, exhibiting distinct characteristics at various loading stages. The failure modes of coal samples of varying sizes exhibited a strong correlation with the spatial distribution of AE events, as well as the strain and displacement field measured by DIC. With the coal sample’s growing size, the percentage of AE high energy events rose while the total number of AE positioning events fell, the proportion of DIC strain concentration area decreased, displacement zoning characteristics became more obvious, and the critical opening displacement of cracks increased. The variance of AE, EMR and DIC related parameters in coal samples showed significant critical slowing down characteristics. Based on their response timescales, EMR energy was identified as an “Early warning”, AE energy as a “Medium warning”, and VE elongation as a “Short-imminent warning”. A comprehensive analysis of multi-parameter monitoring information proved beneficial in accurately pinpointing precursory response points of coal mine dynamic disasters, thereby enhancing monitoring precision.