Prediction of Soil CBR using Machine Learning

The Prediction of California Bearing Ratio (CBR) of Soil Using Machine Learning Models. The soil contains of parameters Fines (F %), Liquid Limit (LL), Plastic Limit (PL), Plastic Index (PI), Optimum Moisture content (OMC %) & Maximum dry density (MDD). The labels of the machine learning model are CBR %. The models that are used in machine learning are Random Forest, Decision Tree, SVR, XG Boost, Multi-Layer Perceptron (MLP) & Artificial Neural Network (ANN). The models were evaluated using metrics such as R² & Mean Squared Error (MSE). The result shows the best model data from the above machine learning models. Out of all these models the Random Forest remains with the greatest accuracy in R² & Mean Squared Error (MSE).