Rock Crack Types Identification by Machine Learning on the Sound Signal

Abstract

Sound signals generated during rock failure contain useful information about crack development. A sound-signal-based identification method for crack types is proposed. In this method, the sound signals of tensile cracks, using the Brazilian splitting test, and those of shear cracks, using the direct shear test, are collected to establish the training samples. The spectrogram is used to characterize the sound signal and is taken as the input. To solve the small sample problem, since only a small amount of sound signal spectrogram can be obtained in our experimental test, pre-trained ResNet-18 is used as a feature extractor to acquire deep characteristics of sound signal spectrograms. Gaussian process classification (GPC) is employed to establish the recognizing model and to classify crack types using the extracted deep characteristics of spectrograms. To verify the proposed method, the tensile and shear crack development processes during the biaxial test are identified. The results show that the proposed method is feasible. Moreover, this method is used to investigate the tensile and shear crack development during the rockburst process. The obtained results are consistent with previous research results, further confirming the accuracy and rationality of this method.