Prediction of low-temperature rheological properties of SBS modified asphalt

Abstract

The extreme learning machine (ELM) algorithm optimized by genetic algorithm (GA) was used to quickly predict the low-temperature rheological properties of styrenic block copolymer (SBS) modified asphalt through the properties of the raw materials. In this work, one hundred groups of survey data and test data were collected and analyzed. Fourteen vital raw material parameters, such as chemical composition indexes of matrix asphalt and technical indexes of SBS modifier, were selected as the input parameter. The stiffness modulus and m-value of SBS modified asphalt were taken as the output parameter. Then, the GA-ELM prediction model of low-temperature rheological properties was established. According to comparison and analysis with other prediction models, the accuracy and output stability of the GA-ELM prediction model were verified. The results show that the GA-ELM model had obvious accuracy and efficiency. It can be used to predict the low-temperature rheological properties of SBS modified asphalt. Compared with the traditional prediction models, the error of the GA-ELM model was reduced by 68.97-81.48%.