Experimental Study of Fracture Characterizations of Rocks under Dynamic Tension Test with Image Processing

Abstract

To investigate the fracture characterizations of rocks under high strain rate tensile failure, a series of dynamic Brazilian tests was conducted using Split Hopkinson pressure bar (SHPB), and a high-speed digital camera at a frame rate of 50,000 frames per second (FPS) with a resolution of 272 × 512 pixels was adopted to capture the real-time images and visualize the failure processes. Using the extracted cracks and image processing technique, the relationship between loading condition (impact velocity), crack propagation process (crack velocity, crack fractal characteristic, and crack morphological features), and dynamic mechanical properties (absorbed energy and strain-stress parameters) was explored and analyzed. The experimental results indicate that (1) impact velocity plays a critical role in both crack propagation process and dynamic mechanical properties, (2) the crack fractal dimension is positively correlated with crack propagation velocity and has a linear relationship with the proposed morphological feature of crack, (3) mean strain rate and max strain of rocks under SHPB loading both decrease with the increase of crack propagation velocity, and (4) the energy absorbed by the rocks increases with increasing impact velocity and has a strong negative correlation with a proposed novel crack descriptor. Experimental studies pertaining to the measurement of crack propagation path and velocity, in particular, some crack feature extraction approaches, present a promising way to reveal the fracture process and failure mechanisms of rock-like materials.

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“…Figure 1 shows the flowchart of the proposed algorithm: the original corrosion image is converted from RGB color space to the HSI color model, followed by the application of histogram equalization and dark detail enhancement method to the component "I." e enhanced image is obtained by combining the enhanced "I" component with the original "H" and "S" components [6] and subsequent conversion to the RGB color space. e whole process is implemented using Matlab.…”

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