Fabrication and numerical verification of two-dimensional random aggregate virtual specimens for asphalt mixture

Liang, Sheng; Luo, Rong

doi:10.1016/j.conbuildmat.2021.122455

Cited by 16 publications

(1 citation statement)

References 23 publications

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“…The image processing technique requires the preparation of the specimens in advance and the scanning by a high-resolution camera or CT technology to obtain two-dimensional or three-dimensional digital images of the specimens, and then a series of image processing to obtain the mesostructure model of asphalt concrete. [1][2][3] Despite a more realistic model, this process requires real specimens as the foundation, and this process is time-consuming and costly. Thus, its application is also limited to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Random aggregate packing algorithm for asphalt concrete based on geographical information systems (GIS)

Liu¹,

Zou²,

Feng³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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The asphalt mixture mesoscopic model is mainly composed of aggregates, asphalt mortar, and the aggregate-asphalt interface layers. Based on Python, the built-in scripting language of ABAQUS finite element software, this paper provides a secondary development of the model to achieve modeling of random aggregate structure and asphalt mixture mesostructure. To solve the problem of aggregate interference, this paper compiles the vector operation of the GIS (Geographic Information Systems) algorithm with Python and applies it to eliminate the problem of aggregate location conflicts and overlaps. Based on the mesoscopic model, this paper simulates the splitting test of asphalt concrete by inserting cohesive units at the interface between aggregates and asphalt and between asphalt units, and the test results match with the actual test results and the simulation results of other scholars, which illustrates the reliability of the asphalt mixture mesoscopic model generated by this method.

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Section: Introductionmentioning

confidence: 99%