Application of Machine Learning Techniques in Modelling Of Soaked and Unsoaked California Bearing Ratio

Abstract

Machine learning techniques such as the Support Vector Machine (SVM), Random Forest (RF), M5 tree, Multiple linear regression and Artificial Neural Network wasadopted to correlate soil physical parameters and California Bearing Ratio (CBR) of soils for Soaked (SCBR) and Unsoaked (USCBR). Four hundred and eighty (480) soil samples were obtained and divided into data set using training and validation of the developed models from some basic soil parameters. Principal Component Analysis (PCA) was implemented to reduce the large dimension of the data set an the actual and predicted values from the models using Root Mean Square Error (RMSE) and coefficient of determination R 2 , it showed