Research on Anisotropic Characteristics of Rock and Intelligent Recognition of Precursory Signal

In order to study the change in anisotropic characteristics of rock materials during the uniaxial compression process, an advanced ultrasonic experimental device and loading device are used to establish an experimental system for measuring ultrasonic waveform in three orthogonal directions in real-time. The experimental results show that there exists pronounced difference between ultrasonic receiving waveform in axial and radial directions. Based on the multifractal theory, multifractal spectrums of receiving waveform are calculated, and multifractal parameters (Δα and Δf) are further analyzed. The multifractal spectrum in the radial direction gradually becomes smaller, but the multifractal spectrum in the axial direction presents an “increase-decrease” trend. During the entire uniaxial compression process, the anisotropic characteristics of the rock sample show an “enlarge-reduce” trend, which is caused by the development and growth of microcracks. Furthermore, a quantitative relationship between multifractal parameter Δα and stress σ is proposed. It indicates that the parameters Δα and σ satisfy the power function. On this basis, a method is proposed that ultrasonic waveform precursor characteristics are recognized automatically using the backpropagation (BP) neural network to monitor the damage state of the rock sample. The application results show that this method can provide an early warning signal more than 100 seconds before the rock sample buckling failure. This research can provide a useful reference for studying the anisotropic characteristics of the rock sample during the uniaxial compression process. The proposed method in this paper has essential meaning for monitoring and early warning of rock stability.