Abstract: Through the comparative analysis on the different predicting methods of HMA elastic modulus, the most reasonable method of fast prediction was found. BP neural network was used to predict HMA elastic modulus at different temperatures, and the relationship between Et/E20 and temperature of HMA elastic modulus was established. On this basis, it was compared with other forecasting methods. The research contents included: the data investigation and the influence factors analysis of HMA elastic modulus, the prediction results of HMA elastic modulus based on different prediction methods, and the prediction results of BP neural network as well as the comparative analysis on other methods. Through the comparative analysis on different predication results, the scientific rationality of BP neural network method was demonstrated. The research shows that BP neural network method can accurately predict HMA elastic modulus at different temperatures; it has a certain advantage compared with other methods.

Key words: highway engineering, HMA elastic modulus, predicting result, BP neural network, comparative analysis

摘要： 通过对比分析沥青混合料弹性模量的不同预测方法，找到一个最合理的快速预测方法。利用BP神经网络方法预测了不同温度的沥青混合料弹性模量 ，建立了沥青混合料弹性模量Et/E20值与温度的关系，在此基础上，与其它预测方法进行对比研究。研究内容包括：沥青混合料弹性模量数据调查及影响 因素分析，不同预测方法的沥青混合料弹性模量预测结果，BP神经网络预测结果与其它方法的对比分析。通过对不同预测结果的对比分析，论证了BP神经 网络方法的科学合理性。研究认为：BP神经网络方法可以准确地预测不同温度的沥青混合料弹性模量，与其它方法相比，具有一定的优势。

关键词: 道路工程, 沥青混合料弹性模量, 预测结果, BP神经网络, 对比分析