Abstract
Due to the heterogeneity of the internal structure and the different external loading conditions, the mechanical and acoustic emission (AE) characteristic parameters of coal and rock are discrete in the process of loading until failure, and many repeated and destructive tests need to be completed to obtain the performance parameters. It is of theoretical significance to explore the correlation of various parameters and to establish multiparameter regression models of coal rock strength and AE characteristics for predicting the strength and acoustic emission characteristic parameters of coal rock and reducing the repeated tests. For the coal sample from a coal seam of Longde Coal Mine in China, the mass density of coal samples and the acoustic velocity in the samples before loading are measured at first, and their respective coefficient of variation is analyzed. Then, the stress-strain curve and the time history curve of AE characteristic parameters are obtained by the uniaxial compression AE test of each coal sample according to the different loading rates. The influence of loading rate, mass density, and acoustic velocity on the mechanical and AE energy parameters of coal sample is analyzed by the section morphology of the coal sample after failure, the three-dimensional location map of AE, and the scanning micrograph of the electron microscope. Based on the least-square method, the multiple regression models of compressive strength, elastic modulus, and the maximum AE energy are established by mass density, acoustic velocity, and loading rate of coal samples. The results indicate that, for the coal samples from the same geological source, the obtained regression models can, respectively, predict the uniaxial compressive strength, elastic modulus, and the maximum AE energy according to the predesigned loading rate, the acoustic velocity, and mass density of coal samples measured before loading, without too many repeated loading failure tests.
Highlights
Due to the heterogeneity of the internal structure and the different external loading conditions, the mechanical and acoustic emission (AE) characteristic parameters of coal and rock are discrete in the process of loading until failure, and many repeated and destructive tests need to be completed to obtain the performance parameters
For the coal sample from a coal seam of Longde Coal Mine in China, the mass density of coal samples and the acoustic velocity in the samples before loading are measured at first, and their respective coefficient of variation is analyzed. en, the stress-strain curve and the time history curve of AE characteristic parameters are obtained by the uniaxial compression AE test of each coal sample according to the different loading rates. e influence of loading rate, mass density, and acoustic velocity on the mechanical and AE energy parameters of coal sample is analyzed by the section morphology of the coal sample after failure, the three-dimensional location map of AE, and the scanning micrograph of the electron microscope
Based on the least-square method, the multiple regression models of compressive strength, elastic modulus, and the maximum AE energy are established by mass density, acoustic velocity, and loading rate of coal samples. e results indicate that, for the coal samples from the same geological source, the obtained regression models can, respectively, predict the uniaxial compressive strength, elastic modulus, and the maximum AE energy according to the predesigned loading rate, the acoustic velocity, and mass density of coal samples measured before loading, without too many repeated loading failure tests
Summary
Coal samples are from Longde Coal Mine of China, and they are processed into standard cylindrical samples with nominal diameter D of 50 mm and height H of 100 mm. e coal samples are marked as M1∼M8. e coal sample size, density ρ, acoustic velocity vs in the coal sample, and the designed loading velocity vc are shown in Table 1 and Figure 1. E coal samples are marked as M1∼M8. E coal sample size, density ρ, acoustic velocity vs in the coal sample, and the designed loading velocity vc are shown in Table 1 and Figure 1. The microstructure of coals after loading was studied by using a JSM6510 scanning electron microscope (SEM). Conduct the breaking lead test of AE Measure sound speed vs in samples Conduct AE experiments during uniaxial compression by designed loading rate vc Obtain compressive strength (σb), stiffness (E), and AE energy (em). Calculate correlation coefficient and analyze the reliability of the model Figure 3: Study scheme
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