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http://hdl.handle.net/10397/96594
Title: | Automated particle shape identification and quantification for DEM simulation of rockfill materials in subgrade construction | Authors: | Bai, H Hu, X Li, R Chen, F Liao, Z |
Issue Date: | 4-Mar-2022 | Source: | Advances in materials science and engineering, 4 Mar. 2022, v. 2022, 5043729 | Abstract: | Rockfill materials, conducted by impermeable stone, are frequently used in subgrade construction projects. The irregularity and variability of particle shape are demonstrated to affect the mechanical properties of rockfill subgrade, such as void ratio and coordination number. This study first identifies the subgrade rockfill particle contour by machine learning algorithms, including AdaBoost, Cascade, and sliding windows. Then, the shape evaluation indexes of length flatness, edge angle, and roughness are quantified, and the statistical analysis of each index is presented. In addition, the discrete element method (DEM) simulation is implemented on the compaction of rockfill subgrade to explore the impact of roundness on characteristics of particles. Finally, the macroanalysis on the void ratio and cumulative settlement and the microanalysis on particle coordination number, rotation momentum, and displacement are studied. The results illustrate that roundness has a significant effect on the mechanical characteristics of subgrade rockfill materials. With the increase of rolling passes, the porosity of packing decreases, whereas the settlement increases gradually. The change rate starts fast and ends slowly. | Publisher: | Hindawi Publishing Corporation | Journal: | Advances in materials science and engineering | ISSN: | 1687-8434 | EISSN: | 1687-8442 | DOI: | 10.1155/2022/5043729 | Rights: | © 2022 Hao Bai et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The following publication Bai, H., Hu, X., Li, R., Chen, F., & Liao, Z. (2022). Automated Particle Shape Identification and Quantification for DEM Simulation of Rockfill Materials in Subgrade Construction. Advances in Materials Science and Engineering, 2022, 5043729 is available at https://doi.org/10.1155/2022/5043729. |
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