The comparison of the performance of ELM, BRNN, and SVM methods for the prediction of compression index of clays

Abstract

The compression index (Cc) is a necessary parameter for the settlement calculation of clays. However, determination of the compression index from oedometer tests takes a relatively long time and leads to a very demanding experimental working program in the laboratory. Therefore, geotechnical engineering literature involves many studies based on indirect methods such as multiple regression analysis (MLR) and soft computing methods to determine the compression index. This study is aimed to predict the compression index by using extreme learning machine (ELM), Bayesian regularization neural network (BRNN), and support vector machine (SVM) methods. The selected variables for each method are the natural water content (wn), initial void ratio (e0), liquid limit (LL), and plasticity index (PI) of clay samples. Many trials were carried out in order to get the best prediction performance with each model. The application results obtained from the models were also compared based on the correlation coefficient (R), coefficient of efficiency (E), and mean squared error (MSE). The results indicate that the BRNN method has better success on estimation of the compression index compared to the ELM and SVM methods.