Randomly Generating the 3D Mesostructure of Soil Rock Mixtures Based on the Full In Situ Digital Image Processed Information

Zhengsheng Li, Haiyang Yi, Cheng Zhu, Zhuang Zhuo, Guoshuan Liu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Article number570
JournalFractal and Fractional
Issue number10
StatePublished - Oct 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analysis
  • Statistical and Nonlinear Physics
  • Statistics and Probability


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