Abstract
The increase of deep underground works has led to many concerns in relation to the dynamic characteristics of rocks subject to heating treatment, including underground rock blasting. For revelation of the dynamic mechanical properties of rocks subject to heating treatment, the granites treated after 25 °C, 100 °C, 200 °C, 300 °C, 400 °C, and 500 °C, respectively, were subject to dynamic tensile and compression tests using split Hopkinson pressure bar (SHPB). Granite specimens treated after different temperatures were compared in respect of dynamic mechanical strength, energy transfer rate, macroscopic and microscopic failure forms, and elastic modulus. The dynamic statistical damage constitutive model of granites subject to heating treatment was constructed with the impact factors relating to temperature introduced to the damage evolution equation for normal temperature statistics and the statistical damage body introduced to the viscoelastic constitutive model. The dynamic fracture characteristics of granites subject to heating treatment were revealed by using XRD and SEM methods to examine the micro-fracture mechanism of these specimens and processing images on Python-Opencv (an open-source library). Results showed the dynamic tensile strength, elastic modulus, and energy transfer rate of granites have a significant temperature effect. The tendency of the model curve is generally consistent with that of the measured stress–strain relation. The relative content of Glimmer in granites gradually increases with temperature, and the fracture mechanism of granites transits from brittle fracture to brittle fracture and local ductile fracture. Through image processing analysis, the peak pore area and crack length of the granites subject to heating treatment are significantly larger than that of the granite specimens at normal temperature.
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Acknowledgements
This work was financially supported by the National Key Research and Development Program of China (No. 2021YFC2902103), the National Natural Science Foundation of China (No. 51934001), and the Basic Scientific Research Expenses of Central Universities (No. 800015Z11A24).
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Appendices
Appendix 1 Histogram equalization
Appendix 2 Convolution kernel processing
Appendix 3 Close operation
Import cv2.
import numpy as np.
from matplotlib import pyplot as plt.
img = cv2.imread("path",1).
kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10)).
kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10)).
kernel3 = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10)).
closing1 = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel1).
closing2 = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel2).
closing3 = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel3).
plt.figure(figsize = (20,15)).
plt.subplot(141),plt.imshow(img),plt.title('Original'),plt.xticks([]), plt.yticks([]).
plt.subplot(142),plt.imshow(closing1),plt.title('RECT'),plt.xticks([]), plt.yticks([]).
plt.subplot(143),plt.imshow(closing2),plt.title('ELLIPSE'),plt.xticks([]), plt.yticks([]).
plt.subplot(144),plt.imshow(closing3),plt.title('CROSS'),plt.xticks([]), plt.yticks([]).
cv2.imwrite("path1",closing1).
cv2.imwrite("path2",closing2).
cv2.imwrite("path3",closing3).
plt.show().
Appendix 4 Skeleton extraction
Import cv2.
from skimage import morphology.
import numpy as np.
img = cv2.imread("path",0).
_,binary = cv2.threshold(img,200,255,cv2.THRESH_BINARY_INV).
cv2.imwrite("path1",binary).
binary[binary = = 255] = 1.
skeleton0 = morphology.skeletonize(binary).
skeleton = skeleton0.astype(np.uint8)*255.
cv2.imwrite("path2",skeleton).
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Wang, Y., Luo, L., Chen, J. et al. Dynamic mechanical behavior and macroscopic and microscopic characteristics of granites subject to heating treatment. Arab J Geosci 16, 112 (2023). https://doi.org/10.1007/s12517-022-11061-x
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DOI: https://doi.org/10.1007/s12517-022-11061-x