Prediction and evaluation the moisture damage resistance of rejuvenated asphalt mixtures based on neural network

Abstract

The aim of this paper is to establish a model for predicting the moisture damage resistance of the rejuvenated asphalt mixture. Based on the surface free energy theory, the contact angels between asphalt and aggregate were measured with known reagents using the sessile drop method. The adhesion work, spalling work and moisture damage resistance index of the asphalt-aggregate interface system were then calculated. Secondly, the tensile strength ratio of the rejuvenated asphalt mixture was determined, and the influence of six indices, including RAP content, rejuvenator content, adhesion work, spalling work, per cent air voids in bituminous mixtures (VV) and freeze–thaw cycle on the performance of the moisture damage resistance of rejuvenated asphalt mixture was analyzed using the gray correlation entropy method. Finally, a three-layer neural network was used to predict the moisture damage resistance of the rejuvenated asphalt mixture. The results show that as the content of aged asphalt increases, the adhesion work of the asphalt- aggregate interface system decreases. Furthermore, for the system of the same rejuvenated asphalt and different aggregates, the rank of adhesion and water resistance from strong to weak is asphalt-limestone＞asphalt- granite, and the rank of the gray entropy correlation between six indices and moisture damage resistance from strong to weak is adhesion work＞spalling work＞VV＞rejuvenator content＞RAP content＞freeze-thaw cycles. Finally, the accuracy of the neural network predicting the moisture damage resistance of rejuvenated asphalt mix reaches 96.188%, which provides a new method to predict the moisture damage resistance of rejuvenated asphalt mixture.