Optimization design and verification of Large Stone Porous asphalt Mixes gradation using Compressible Packing Model. (10th January 2020) - Yuan, Gaoang; Hao, Peiwen; Li, Dewen; Pan, Junli; Dong, Shi
Graphic abstract: Highlights: Compressible Packing Model was utilized in asphalt mixes gradation optimization design. An innovative gradation optimization method was proposed considering the packing density. The testing asphalt mixes were fabricated according to the optimized gradation. The anti-reflection cracking performance was verified by the Overlay Tester. The characteristics of pore structures were analyzed by X-ray ICT. Abstract: As a unique gradation type of asphalt mixtures, Large Stone Porous asphalt Mixes (LSPM) has advantages in promoting pavement performance including cracking resistance, rutting resistance, and moisture susceptibility. The gradation design is a critical determining factor for the LSPM performance. Therefore, this paper aims at optimizing the gradation design of LSPM, as well as testing its mechanical performance and microstructural characteristics. Firstly, a new gradation of LSPM is designed and optimized on basis of the Compressible Packing Model (CPM) theory. Then, the basic pavement performance of the new LSPM are verified, and compared to an existing LSPM. The cracking resistance of the new and existing LSPM is measured by the Overlay Tester. Meanwhile, the pore structure characteristics are analyzed for further elaborating the differences between the two mixes. The result shows that the CPM model can simulate packing state. The new LSPM gradation is proposed using the CPM and correction coefficient k . The first cycle load loss rate P 1 and loading cycles of 50% load loss rate N 0.5 can evaluate the anti-reflection cracking performance more effectively. Air void content of LSPM in vertical direction is heterogeneity distribution. The macropores and extra-macropores are major components of pores, above 80% of all. It indicates that the CPM method is applicable in LSPM gradation design. Moreover, performance of the optimized LSPM have been significantly improved.