Prediction of california bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine

Expansive soil possess low California bearing ratio (CBR) value other than the peculiar swell-shrink behaviour, hence it is stabilized using admixtures, to be suitable for construction of pavements. In this paper models have been developed for prediction of CBR of an expansive soil stabilized with lime and quarry dust at different curing periods, using artificial neural network (ANN) and support vector machine (SVM). The accuracy of the predictive models has been compared. It is found that both the ANN and the SVM models are very accurate in prediction of CBR of stabilized expansive soil and the performances of the SVM models are found to be better than that of the ANN models. © 2015 ejge.