Study of adhesion between crack sealant and pavement combining surface free energy measurement with molecular dynamics simulation

摘要

Hot-applied crack sealants are generally used for the repair of asphalt pavement cracks. However, adhesion failure between sealant and pavement frequently occurs during the service period. For the purpose of material optimization, the selection of sealant is usually carried out through the macro performance test, which is time-consuming and labor-consuming. In addition, the mechanism of adhesion failure is difficult to be figured out merely through the macro test. Therefore, quantitative methods, from a microscopic perspective, have been proposed aiming to realize the quick identification of adhesion performance. In this study, surface energy measurement and molecular dynamics simulation were used to quantitatively evaluate the adhesion between sealant and asphalt mixtures. Specifically, the influences of asphalt type, aggregate type, sealant type, and mixture gradation on the adhesive properties were investigated. The work of adhesion between sealant and asphalt mixtures was obtained based on weighted-average calculation for sealant and various phases (aggregate, asphalt mastic, and air void) and the influence of heterogeneity in mixtures was also considered. Moreover, the macroscopic pull-off test was performed to validate the findings from surface energy measurement and molecular dynamic simulation. The results showed that surface energy measurement, molecular dynamic simulation, and pull-off strength exhibited high consistency in the adhesion evaluation and showed good correlations with each other. Therefore, the macroscopic properties can be predicted by degrading scale methods (molecular dynamics simulation and surface free energy measurement). Further, the surface energy measurement and molecular dynamic simulation can be utilized to choose the appropriate crack sealant for a specific asphalt pavement, which can provide an alternative method for the selection of the materials and avoid extensive performance tests. (C) 2019 Elsevier Ltd. All rights reserved.