Support vector regression to predict the performance of stabilized aggregate bases subject to wet-dry cycles

Durability is a notion that is integrated with the performance of stabilized pavement materials. Also, because it can be quantified and measured, it carries significant influence on the design of pavements. This study focuses on using support vector machine, a machine learning algorithm, in assessing the performance of stabilized aggregate bases subject to wet-dry cycles. Support Vector Regression (SVR) is a statistical learning algorithm that is applied to regression problems and is gaining popularity in pavement and geotechnical engineering. In our study, SVR was shown to be superior to the least-squares (LS) method. Results of this study show that SVR significantly reduces the mean-squared error (MSE) and improves the coefficient of determination (R ²) compared to the widely used LS method.