Dynamic modulus prediction model and analysis of factors influencing asphalt mixtures using gray relational analysis methods

Abstract

In the design of asphalt pavement, the dynamic modulus of the asphalt mixture is an indispensable parameter used for checking the fatigue cracking and permanent deformation of the mixture. However, there are many parameters affecting the dynamic modulus. Designers often do not know how to choose among them, and obtaining the parameters required in the traditional prediction model usually require sophisticated instruments, which is not conducive to their development and use by road designers. In this study, the method of gray correlation analysis was used to screen out the important and easy-to-obtain material parameters of an asphalt mixture, and then the multiple linear regression equation was used to establish a prediction model suitable for obtaining the dynamic modulus of asphalt mixtures in Jilin province. Tested against the estimated data, the fitting result is good. This research demonstrates that gray relational theory and multiple linear regression analysis can be applied in the establishment of a dynamic modulus model.