Improving the calculation accuracy of FEM for asphalt mixtures in simulation of SCB test considering the mesostructure characteristics

Fracture simulation of asphalt mixtures based on three-dimensional finite element (3D-FE) simulation considering the mesostructure consumes too much computing resources and has low efficiency, which limits the popularisation and application of asphalt mixture fracture characteristics simulations. In contrast, two-dimensional finite element (2D-FE) simulation needs less computational resources and has high efficiency. However, 2D-FE simulation results significantly impacted by aggregate content and particle distribution in the scanned images and an arbitrary 2D image could hardly reflect the mesostructure of the whole asphalt mixture specimen. Therefore, two internal structure indicators, such as aggregate content indicator (ACI) and aggregate distribution indicator (ADI), were defined for image assessment, and an image selection process was proposed based on these two indicators for 2D-FE modelling method. By comparing the results obtained from 3D-FE simulations, 2D-FE simulations with or without image selection process, and laboratory tests, it can be found that the results of 2D-FE simulations with the image selection process are more consistent with laboratory test than other two simulation processes. Thus, it can be verified that the image selection process can highly enhance the accuracy of 2D-FE simulation while reducing the number of models needed and improving computational efficiency.

Automated summary

The traditional three-dimensional finite element simulation of asphalt mixture fracture consumes too many computing resources and has low efficiency, limiting its application. In contrast, the two-dimensional finite element simulation is more efficient but is affected by aggregate content and particle distribution, which fails to accurately represent the real particle structure. By using an image selection process, the accuracy of the two-dimensional finite element simulation can be improved while reducing the number of models required and computational resources.