Study on the evolution of acoustic signals and strain evolution during large-scale tensile failure test of limestone

Abstract

[Objective] Tension rupture represents the predominant rupture form in the process of rock collapse, with the potential to cause significant disruption. Consequently, there is a clear need to study the acoustic emission signal characteristics and strain evolution law in the process of rock tension damage. This will facilitate the identification of precursor information of rock damage, which can then be used for the monitoring and early warning of rock collapse. [Methods] A three-point bending test(1.0 m×0.5 m×0.15 m) was conducted on a large-scale tuff to monitor the rupture process in real time using acoustic emission technology and digital image correlation technology. The acoustic emission signals and deformation evolution characteristics of large-scale tuff in the process of tensile damage were then analyzed by combining principal component analysis and hierarchical clustering algorithms. [Results] The application of acoustic emission detection technology allows for the accurate identification of the damage state of the rock mass, as well as the provision of effective damage precursor information. [Conclusion] The result show that:(1) According to the evolution of acoustic signal parameters, the tensile failure of rock can be divided into four stages: microcrack initiation stage, small-scale and stable cracking stage, unstable cracking stage and failure stage;(2) The acoustic signals can be divided into six categories using the acoustic emission parameters using clustering and principal component analysis algorithm. The cumulative changes and proportions of the two types of characteristic signals, the gradual low-amplitude type signal and the sudden high-amplitude type signal, are capable to characterize the cracking process;(3) The findings indicate that the acoustic emission characteristic parameters and their evolution characteristics can effectively reflect the rock rupture process, providing precursor information for rock damage up to 141 seconds in advance compared with the simultaneous deformation monitoring. The result of this study offer insights that can inform the development of effective method and techniques for monitoring and early warning of rock failure. This research could provide support for rock collapse monitoring and early warning.

Keywords

acoustic emission / machine learning / digital image correlation / failure precursor / rock collapse / deformation / influencing factors