Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.

No abstract

To further improve the durability of cement-stabilized macadam and guarantee the use quality and sustainability of a semi-rigid base, the current study was carried out. With the help of a dry shrinkage test, temperature shrinkage test, freeze–thaw bending test, and fatigue test, the effect of incorporating PVA fiber on the deformation characteristics of cement-stabilized macadam was analyzed, and the changes in low-temperature residual toughness of the mixture before and after modification were compared. The low-temperature toughness of PVA fiber cement-stabilized macadam was evaluated with the help of the standard toughness evaluation method. The fatigue life prediction equation of PVA fiber cement-stabilized macadam was established based on the Weibull distribution. The results showed that PVA fiber can effectively improve the deformation characteristics, low-temperature toughness, and fatigue performance of cement-stabilized macadam. The low-temperature residual flexural tensile strength and low-temperature bearing capacity were increased by 10.3% and 55.3%, respectively. The residual toughness indices were increased by 58.6%, 88.1%, and 98.3% and the residual strength index was increased by more than 100%. The fatigue life was improved by 178~368% under different stress intensity ratios. The fatigue life values obeyed the two-parameter Weibull distribution, and the correlation between the fatigue life prediction equation and the measured data was significant. The fatigue life prediction error was between 0.03 and 4.9% under different stress intensity ratios.

No abstract