A new method for determining the crack classification criterion in acoustic emission parameter analysis

At a microscopic scale, the failure of brittle materials results from crack initiation, propagation and coalescence. Acoustic emission (AE) technique, especially parameter analysis, has been widely applied to investigate cracking process and mechanism in civil engineering. However, crack classification in AE parameter analysis mostly derives from the empirical relation between the RA value and the average frequency, and the crack classification criterion, i.e., the optimal transition line between shear and tensile cracks in the parameter analysis has not been determined yet. Based on statistical analysis of dominant frequency characteristics of AE signals, a new method is proposed for determining the optimal transition line for crack classification in AE parameter analysis. Spectrum analyses of AE waveform data in the representative specimens are carried out to acquire the dominant frequency of AE waveforms. Proportions of waveforms distributed in low and high dominant frequency bands (L-type and H-type waveforms) are determined. The ratios of tensile and shear cracks, viewed as measurements, are determined by the statistical analysis of dominant frequency characteristics of AE waveforms. For a series of different transition line, the predicted ratios of tensile and shear cracks in AE parameter analysis are determined. The optimal transition line is determined to be the one corresponding to the least square difference between predicted data and measurements. The determined optimal transition line can be directly applied for crack classification in AE parameter analysis in the subsequent experiments of this brittle material. The reliability of the proposed method were validated by laboratory tests of rock subjected to compression. It can be found that the optimal transition line in the parameter analysis is approximately from 1:100 to 1:500 for brittle rock under compression. The findings in this study contributes to the enhancement of the accuracy and efficiency of AE source mechanism and damage process analysis.