A Simple Approach for Generating Random Aggregate Model of Concrete Based on Laguerre Tessellation and Its
Yutai Guo1, Jialong He1, Hui Jiang1
1State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China.
Materials (Basel, Switzerland)
|September 9, 2020
Summary
This study introduces a simple Laguerre tessellation strategy for generating random aggregate models (RAMs) in concrete. The method efficiently creates models for mesoscopic analysis, improving understanding of aggregate effects on concrete elasticity.
Area of Science:
- Materials Science
- Computational Mechanics
- Civil Engineering
Background:
- Mesoscopic modeling of concrete requires accurate random aggregate models (RAMs).
- Challenges include aggregate geometry, gradation, and high volume ratios, hindering efficient numerical construction.
- Existing methods struggle with the complexity of concrete meso-structures.
Purpose of the Study:
- To present a simple and efficient strategy for generating RAMs of concrete.
- To enable direct use of generated models for numerical concrete sample construction.
- To investigate the influence of meso-structural properties on concrete elasticity.
Main Methods:
- Laguerre tessellation for initial model generation.
- Geometric smoothing and scaling for refinement.
- Scaled Boundary Finite Element Method (SBFEM) for stress analysis.
- Image-based octree meshing for automatic analysis setup.
Main Results:
- The proposed approach generates computer-assisted design (CAD) files of RAMs.
- Successfully reproduced the trend of increasing elastic modulus with coarse aggregate grading.
- Demonstrated the effectiveness of the Laguerre tessellation strategy for concrete mesoscopic modeling.
Conclusions:
- The Laguerre tessellation-based strategy offers a simple and effective method for RAM generation.
- The approach facilitates accurate mesoscopic analysis of concrete behavior.
- This method aids in understanding the relationship between concrete meso-structure and its elastic properties.
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