Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method

Abstract

The method for precursor information acquisition based on acoustic emission (AE) data for jointed rock masses is of significant importance for the early warning of dynamic disasters in underground engineering. A clustering-convolutional neural network (CNN) method is proposed, which comprises a clustering component and a CNN component. A series of uniaxial compression tests were conducted on granite specimens containing a persistent sawtooth joint, with different strain rates (10–5–10–2 s–1) and joint inclination angles (0°–50°). The results demonstrate that traditional precursory indicators based on full waveforms are effective for obtaining precursor information of the intact rock failure. However, these indicators are not universally applicable to the failure of rock masses with a single joint. The clustering-CNN method has the potential to be applied to obtain precursor information for all three failure modes (Modes I, II and III). Following the waveform clustering analysis, the effective waveforms exhibit a low main frequency, as well as high energy, ringing count, and rise time. Furthermore, the clustering method and the precursory indicators influence the acquisition of final precursor information. The Birch hierarchical clustering method and the S value precursory indicator can help to obtain more accurate results. The findings of this study may contribute to the development of warning methods for underground engineering across faults.