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| Multi-scale analysis of three-dimensional morphological characteristics of graded gravel on high-speed railway |
| LU Rui1，2，LUO Qiang1，2，LIU Gang3，ZHAO Chunfa4，HE Xinyi3 |
| (1. School of Civil Engineering，Southwest Jiaotong University，Chengdu，Sichuan 610031，China；2. MOE Key Laboratory of High-Speed Railway Engineering，Southwest Jiaotong University，Chengdu，Sichuan 610031，China；3. School of Civil Engineering，Architecture and Environment，Xihua University，Chengdu，Sichuan 610039，China；4. State Key laboratory of Traction Power，Southwest Jiaotong University，Chengdu，Sichuan 610031，China) |
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Abstract The particle morphology of graded gravels of high-speed railway subgrade has a significant impact on the macro-mechanical properties. Reasonably quantifying the three-dimensional morphological characteristics of the particles is the basic work of the research on the micro-mechanism of fillers. Based on the laser scanner，the point cloud data of 700 gravel particles in 7 particle groups in the range of 2.36–31.5 mm are acquired. And these data are reconstructed to form three-dimensional grid models. By introducing the root mean square curvature (RMS) which describes the surface unevenness of the particles and considering the particle mesh model smoothing process and the RMS curvature distribution characteristics，the angularity parameters are defined by the proportion of the point cloud quantity in the edge area，and the texture parameters are defined by the difference of the model before and after smoothing. Combined with the shape parameters described by the relationship between the long，middle and short axes，the indexes system to describe the multi-scale morphological characteristics of gravel particles is formed. By selecting the particles with typical morphological characteristics，it is verified that the established index system has a good morphology discrimination ability. According to the statistical analysis of the scanned gravel particles，the shape，angularity and texture of the gravel particles in different groups basically present the characteristics of normal distribution. As the particle size increases，the proportion of block particles increases，and the angularity and the texture characteristics are more prominent. The research results can provide a reference for the quantitative evaluation of particle morphological characteristics from macro，medium and micro scales.
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2022, Vol. 41 
