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Abstract
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.
Highlights
Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. e 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
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
Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. e SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture
Summary
Recommended by the Strategic Highway Research Program (SHRP) that can be applied with offset sine wave or half-sine wave load is used for dynamic modulus testing of highmodulus asphalt mixtures. E test temperature is 15°C. e loading frequency is 10 Hz. e dynamic modulus of the high-modulus asphalt mixture is measured at the condition of unconfined specimens. E dynamic modulus of the high-modulus asphalt mixture is selected as the output factor (15°C, 10 Hz). When constructing the neural network prediction model, in order to avoid problems such as the large difference between the sample data and the failure of the network to converge or the extension of the training time, the sample data are standardized according to the following equation: Xi. where Xi is the standardized data, X is the sample data, Xmin is the minimum value of the sample data, and Xmax is the maximum value of the sample data
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