An Efficient Construction Method of the 3D Random Asphalt Concrete Model Based on the Background Grid and the Moving-and-Densifying Algorithm

• Published in: Buildings (Basel) 2023-04, Vol.13 (4), p.990
• Main Authors: Liu, Xiaoming, Chen, Huaan, Zhao, Yu
• Format: Article
• Language: English
• Subjects: Aggregates; Algorithms; Analysis; Asphalt; asphalt concrete; background grid method; Concrete; Construction; Construction methods; Efficiency; Electric properties; Finite element method; Grid method; Heat conductivity; Heat transfer; Mathematical models; Methods; Mixtures; moving-and-densifying algorithm; Prediction models; random model; Stability; Thermal conductivity; Three dimensional models; Two dimensional models
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings13040990

In order to avoid the tedious and time-consuming measuring process for thermal conductivity, many random models have been proposed, but the construction of those random models is still inefficient, which limits the further application. In this paper, a construction method of three-dimensional random asphalt models for predicting thermal conductivity based on the background grid and the moving-and-densifying algorithm was proposed which greatly improves construction efficiency. The influence of the random factors on models’ stability was studied and the range of the key factors within all random factors was restricted. Further, a conflict judgment method for the convex aggregate and the improved take-and-place method based on the background grid method and the moving-and-densifying algorithm was realized by MATLAB code to construct aggregate mixture models. Finally, the aggregate mixtures model was imported into ABAQUS 2022 to predict the thermal conductivity based on the steady-state plate method, and the validity of the predicting result was verified by experimental result. With this construction method, the stability index is improved by more than 80.71%, and packing efficiency is 198.98% higher than before. Additionally, the 3D random model showed a smaller prediction error range (less than 5%) than the 2D models (more than 10%) and was more accurate than the 2D prediction model. This research focused on improving the construction efficiency of the 3D random asphalt concrete model which contributes to full utilization and laying a foundation for further improvement.