TY - JOUR
T1 - Randomly Generating the 3D Mesostructure of Soil Rock Mixtures Based on the Full In Situ Digital Image Processed Information
AU - Li, Zhengsheng
AU - Yi, Haiyang
AU - Zhu, Cheng
AU - Zhuo, Zhuang
AU - Liu, Guoshuan
N1 - Publisher Copyright: © 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - Understanding the occurrence and evolution of geological disasters, such as landslides and debris flows, is facilitated by research on the performance of soil rock mixes (SRM). Recently, more and more researchers have been interested in studying the mesostructure reconstruction process of SRM. The present mesostructure generation approaches, however, have several weaknesses. One of the weaknesses is that they do not consider the impact of particle shape and therefore cannot ensure similarity to the in situ SRMs. In this study, a new mesostructure generation method that randomly generates SRMs based on the full in situ digital image processing (DIP) information is proposed. The generation procedure of the proposed algorithm considers the geometry characteristics of in situ SRMs, including the size distribution, particle shape, and 2D fractal dimension of the cross-section. A parametric study was performed to examine how the rock content and particle shape affected the fractal dimension of the generated SRMs. The results indicate that as the rock content increases in intensity, the fractal dimension also increases. Only when the angular particle content is less than 75% does it affect the fractal dimension. The fractal dimension of the generated mesostructures increases with the increase in the angular particle proportion under the same rock content.
AB - Understanding the occurrence and evolution of geological disasters, such as landslides and debris flows, is facilitated by research on the performance of soil rock mixes (SRM). Recently, more and more researchers have been interested in studying the mesostructure reconstruction process of SRM. The present mesostructure generation approaches, however, have several weaknesses. One of the weaknesses is that they do not consider the impact of particle shape and therefore cannot ensure similarity to the in situ SRMs. In this study, a new mesostructure generation method that randomly generates SRMs based on the full in situ digital image processing (DIP) information is proposed. The generation procedure of the proposed algorithm considers the geometry characteristics of in situ SRMs, including the size distribution, particle shape, and 2D fractal dimension of the cross-section. A parametric study was performed to examine how the rock content and particle shape affected the fractal dimension of the generated SRMs. The results indicate that as the rock content increases in intensity, the fractal dimension also increases. Only when the angular particle content is less than 75% does it affect the fractal dimension. The fractal dimension of the generated mesostructures increases with the increase in the angular particle proportion under the same rock content.
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U2 - 10.3390/fractalfract6100570
DO - 10.3390/fractalfract6100570
M3 - Article
SN - 2504-3110
VL - 6
JO - Fractal and Fractional
JF - Fractal and Fractional
IS - 10
M1 - 570
ER -