91 / 2023-06-14 18:10:13

Study on precursor features of coal and rock loading failure based on difference network

difference network,coal and rock failure,spatio-temporal evolution dynamical system,precursor features,damage characterization

In order to reveal the spatio-temporal precursor features of coal and rock loading failure, and accurately identify the critical state of coal and rock failure, the occurrence and development of coal and rock loading failure is regarded as the spatio-temporal evolution dynamical process of the complex network system, and the spatio-temporal evolution dynamical characteristics of coal and rock dynamic disasters are analyzed. The precursor features indicators I(t) and I_a(t) of the coal and rock failure based on the difference network are proposed, Brazilian splitting experiments of different types of coal and rock are carried out. Combined with the damage theory, the load model of coal and rock damage characterization based I_a(t) is established. The results show that when the coal-rock system is in the warning critical period, the damage is accelerated, with high potential energy, low rebound and weak robustness, which is reflected in the acoustic emission (AE) and electromagnetic radiation (EMR) data at different spatial monitoring points. When the coal-rock system is in the stable stage, the structure of the correlation network at adjacent moment is similar, and the number of edges in the difference network is small. While when coal or rock is about to fracture, the correlation network at adjacent moment has great structure differences, and there are many edges in the difference network. I(t) and I_a(t) synthesize the signal characteristics of different spatial monitoring points, and reflect the spatio-temporal evolution process and damage state of coal and rock. The damage characterization load based on I_a(t) is in good agreement with the measured load, and the correlation coefficient between the measured load and the calculated load based on I_a(t) of most coal and rock samples is more than 0.80. The research results can be used as the precursor features of coal and rock failure, and have certain significance for the early warning of coal and rock dynamic disasters.