Investigation of Applying Support Vector Machine Regression on Resilient Modulus of Stabilized Pavement Layers

Resilient modulus (MR) crucially plays a significant role in pavement designing in accordance with mechanistic–empirical method. Furthermore, since the utilization of different pavement stabilizers are generally established in road projects, acquiring the resilient modulus of treated pavement layers appears to be inevitable. However, due to the complexity, time-consuming and equipment requirements for repeated load testing, several methods have been proposed to apply. Apart from the empirical formulas and multifarious regression models, the novel artificial intellect algorithms have been developed to evaluate the accurate value of (MR) of treated pavement layers. In this paper, the comparisons of the different artificial intellect algorithms such as Support Vector Machine Regression (SVR), Artificial Neural Network(ANN) and conventional linear and non-linear models have been substantially stated. Eventually, due to less sensitive to dataset input distribution, high accurate estimation, avoiding the black box and high adoptability to various dataset inputs, the SVR is selected as the most appropriate prediction model for (MR) value evaluation.

Keywords:

Resilient Modulus , Treated Pavement Layers , Artificial Intellect Algorithm , Support Vector Machine