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A Simple Approach for Generating Random Aggregate Model of Concrete Based on Laguerre Tessellation and Its Application Analyses

机译:基于Laguerre Telsellation的混凝土随机聚集模型及其应用分析的一种简单方法

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摘要

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.
机译:生成随机聚合模型(RAM)在混凝土复合材料的介于介质建模中起关键作用。聚集体的任意几何,宽级和大体积比为混凝土中型结构的快速有效数值构建构成了巨大挑战。本文提出了一种基于Laguerre Telsellation的混凝土的RAM的简单策略,主要由三个步骤组成:镶嵌,几何平滑和缩放。通过所提出的方法获得的计算机辅助设计(CAD)rams的RAMS档案可以直接采用随机数混凝土样本的构建。结合基于图像的Octree型号算法,采用缩放边界有限元方法(SBFEM)进行质量混凝土样本的自动应力分析,并进行参数研究以研究对混凝土弹性性质的思结构作用。建模结果成功地复制了粗聚集在文献测试数据中的粗聚集体的增加趋势,并证明了提出的策略的有效性。

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