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.

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高模量沥青混合料的动态模量预测

动态模量是高模量沥青混合料的关键评价指标，但其测试和数据采集相对困难。目的是实现高模量沥青混合料动态模量的准确预测，进一步优化高模量沥青混合料的设计过程。选取了高模量沥青及其混合料的五个高温性能指标。分析了上述五个指标与高模量沥青混合料动态模量的相关性。在此基础上，通过多元回归、广义回归神经网络（GRNN）和支持向量机（SVM）神经网络建立了基于小样本数据的高模量沥青混合料动态模量预测模型。根据参数调整和交叉验证，对不同预测模型的输出稳定性和准确性进行了比较和评估。推荐了最有效的预测模型。结果表明，SVM模型比多元回归模型和GRNN模型具有更显着的预测精度和输出稳定性。其预测误差为 0.98-9.71%。与其他两个模型相比，SVM模型的预测误差分别下降了0.50-11.96%和3.76-13.44%。推荐使用 SVM 神经网络作为高模量沥青混合料的动态模量预测模型。其预测误差为 0.98-9.71%。与其他两个模型相比，SVM模型的预测误差分别下降了0.50-11.96%和3.76-13.44%。推荐使用 SVM 神经网络作为高模量沥青混合料的动态模量预测模型。其预测误差为 0.98-9.71%。与其他两个模型相比，SVM模型的预测误差分别下降了0.50-11.96%和3.76-13.44%。推荐使用 SVM 神经网络作为高模量沥青混合料的动态模量预测模型。