关键词: 红外光谱, 道路石油沥青, 老化沥青, 主成分分析, BP神经网络

Abstract: Based on the analysis of Infrared Spectroscopy curve obtained from different traits asphalts, we used the principal component analysis to make a cluster analysis on the spectrum data, and established the discrimination model for different asphalt with different traits by using the extracted principal component as the input value of the BP neural network. We took the first three principal components of the model as the input variables of the neural network, which accelerated neural network training of and improved the precision of the model. We randomly selected total 110 data samples from asphalt samples from each kind of trait to form a training set, the remaining 30 samples formed the prediction set. Then we established the training model and used the samples of prediction set to verify the model, and determined the standard deviation as ± 0.01. The result showed that only one unknown sample beyond the deviation range and the correct rate of this method was 96.7%. The satisfactory result suggested that the Infrared Spectroscopy curve had excellent effect on classification and identification, and provided a new method for the rapid identification of different traits of asphalts.

Key words: Infrared Spectroscopy, asphalt, aged asphalt, principal component analysis, BP neural network