Prediction of Resilient Modulus of Lime-Treated Subgrade Soil Using Different Kernels of Support Vector Machine

Abstract

The resilient modulus (MR) plays a crucial role in mechanistic–empirical design such that acquiring the MR of lime-treated pavement layers seems to be necessary, because the use of lime materials in road projects is generally established. However, because of the complexity of and time and equipment requirements for repeated and cyclic load testing, several methods have been proposed to apply. In this paper, the novel artificial intelligence algorithm called support vector machine regression (SVR) has been applied to evaluate accurate values of lime-treated pavement layers’ MR. Moreover, polynomial kernel, radial basis function, and linear kernel as three different kernels of SVR were used to predict the MR of lime-treated subgrade soil. To create the model and validate the algorithm’s performance, approximately 75% of the data was selected as training data sets, and the remaining ones were applied as testing data sets. For this study, the obtained results indicate that developed SVR models produce high-performance predictions, and the polynomial kernel is selected with the significant correlation coefficient (R2) value of 98% for predicting the MR of lime-treated soil.