Support vector machine to predict the indirect tensile strength of foamed bitumen-stabilised base course materials

Abstract

Indirect tensile strength (ITS) crucially plays a significant role as a main feature for mix design of foamed bitumen-stabilised base course layers. Due to time-consuming and equipment requirements for conducting the ITS test, it appears to be demanded to develop the numerical models in order to predict the ITS of a foamed bitumen-stabilised base course. In this paper, the novel artificial intelligence algorithm of support vector machine regression (SVR) has been applied to predict the accurate value of indirect tensile strength (ITS) of the foamed bitumen-stabilised base course. Moreover, two kernels of SVR including the polynomial kernel and radial basis function (RBF) kernel have been investigated to predict the accurate ITS values of both unsoaked and soaked foamed bitumen-stabilised base separately. In order to create the model and to validate the algorithm performance, about 80% of data were randomly selected as the training data set and the remaining ones applied as the testing data set. The obtained results indicate that the developed SVR models produce a high prediction ability for this study. In addition, the obtained predicting correlation coefficients (R2) of polynomial and RBF kernels for both unsoaked and soaked samples were compared individually and, eventually, the RBF kernel of the SVR model was selected as the most accurate computational predicting model.