Abstract
This paper demonstrates the application of support vector machine (SVM) applied to ATR-FTIR data to solve classification and regression problems associated with rapid determination of PG grade for SBS modified asphalt. The modified asphalt samples were produced by mixing three kinds of matrix asphalt, two kinds of SBS and five kinds of SBS content from 2% to 6% under the same manufacture process. Therefore a total of 150 data sets were evaluated with five parallel tests for each sample. In the SVR model, the ATR-FTIR data were parameter for the input layer whereas the PG parameters of asphalt such as, rut factor, creep stiffness and creep rate were output layer. While in the SVC model, high temperature grade and low temperature grade were output layer. This new method allows rapid determination of multi-properties from a single spectrum for SBS modified asphalt and it is promising for online material monitoring of specific project.
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