Guo Yutai, He Jialong, Jiang Hui, Zhou Yuande, Jin Feng, Song Chongmin
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China.
School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Materials (Basel). 2020 Sep 3;13(17):3896. doi: 10.3390/ma13173896.
Generating random aggregate models (RAMs) plays a key role in the mesoscopic modelling of concrete-like composite materials. The arbitrary geometry, wide gradation, and high volume ratio of aggregates pose a great challenge for fast and efficient numerical construction of concrete meso-structures. This paper presents a simple strategy for generating RAMs of concrete based on Laguerre tessellation, which mainly consists of three steps: tessellation, geometric smoothing, and scaling. The computer-assisted design (CAD) file of RAMs obtained by the proposed approach can be directly adopted for the construction of random numerical concrete samples. Combined with the image-based octree meshing algorithm, the scaled boundary finite element method (SBFEM) was adopted for an automatic stress analysis of mass concrete samples, and a parametric study was conducted to investigate the meso-structural effects on concrete elasticity properties. The modelling results successfully reproduced the increasing trend of concrete elastic modulus with the grading of coarse aggregates in literature test data and demonstrate the effectiveness of the proposed strategy.
生成随机聚集体模型（RAMs）在类混凝土复合材料的细观建模中起着关键作用。骨料的任意几何形状、宽级配和高体积比给混凝土细观结构的快速高效数值构建带来了巨大挑战。本文提出了一种基于拉盖尔镶嵌的混凝土随机聚集体模型生成的简单策略，该策略主要包括三个步骤：镶嵌、几何平滑和缩放。通过该方法获得的随机聚集体模型的计算机辅助设计（CAD）文件可直接用于构建随机数值混凝土样本。结合基于图像的八叉树网格划分算法，采用比例边界有限元法（SBFEM）对大体积混凝土样本进行自动应力分析，并进行参数研究以探讨细观结构对混凝土弹性性能的影响。建模结果成功再现了文献试验数据中混凝土弹性模量随粗骨料级配增加的趋势，并证明了所提策略的有效性。
