Estimation of California Bearing Ratio of Soils Using Random Forest based Machine Learning

Abstract

California Bearing Ratio (CBR) is an essential parameter utilized to evaluate the strength of the soil subgrades and base course materials of different types of pavements. In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory. An experimental database was collected from 214 soil samples, which were classified according to AASHTO M 145(clayey, gravel, sand, silty and clayey soils). The data was divided into 70% training and 30% test data in the model study. Model performance was evaluated using standard statistical measures such as coefficient of determination, correlations, and errors (relative error, MAE, and RMSE). Based on the analysis results shows the RF model is capable of correct prediction of the CBR of the Soil.