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

ABSTRACT

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.

KEYWORDS:

• Asphalt mixture
• internal structure
• semi-circular bending test simulation
• image-based modelling technology
• X-ray CT scanning

Acknowledgements

All the authors of the following references are much appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors gratefully acknowledge the support of the National Natural Science Foundation of China (NO. 51808115), Cyan and Blue Talent Training Project of the Colleges and Universities in Jiangsu Province, Science foundation of Nanjing Vocational Institute of Transport Technology (Project No. JZ1802), 333 High-Level Talent Training Project of Jiangsu Province.