Extreme Learning Machine Based Modeling of Resilient Modulus of Subgrade Soils

This paper investigates the potential of two variants of extreme learning machine based regression approaches in predicting the resilient modulus of cohesive soils. Support vector regression was used to compare the performance of the proposed extreme learning machine based regression approaches. The dataset used in this study was derived from literature and consists of 9 input parameters with a total of 891 cases. For testing, two methods i.e. train/test and tenfold cross validation was used. In case of train and test methods, a total of 594 randomly selected cases were used to train different algorithms and the remaining 297 data were used to test the created models. Correlation coefficient value of 0.991 (root mean square error = 3.47 MPa) was achieved by polynomial kernel based extreme learning machine in comparison to 0.990 and 0.990 (root mean square error = 4.790 and 4.290 MPa) by simple extreme learning machine and radial basis kernel function based support vector regression respectively with test dataset. Comparisons of results with tenfold cross validation also suggest that polynomial kernel based extreme learning machine works well in terms of root mean square error and computational cost with the used dataset. Sensitivity analysis suggests the importance of confining stress and deviator stress in predicting the resilient modulus when using with polynomial kernel based extreme learning machine modeling approach.