Obtaining the California Bearing Ratio Prediction via Hybrid Composition of Random Forest

Artificial intelligence algorithms have become much more sophisticated, so the most complex and challenging problems can be solved with them. California Bearing Ratio (CBR) is a timeconsuming testing parameter, and univariate and multivariate regression methods are used to address this challenge. Therefore, the CBR value is an essential parameter in indexing the resistance provided by a structure's subterranean formation or foundation soil. CBR is a crucial factor in pavement design. However, its determination in laboratory conditions can be a time-consuming process. This makes it necessary to look for an alternative method to estimate CBR in the soil subgrade, especially the developed layers of the soil. This study has developed one of the machine learning (ML) models, including Random Forest (RF), to predict the CBR. Additionally, some meta-heuristic algorithms have been used for improving the accuracy and optimizing the output of the prediction, consisting of Gold Rush optimizer (GRO), Stochastic Paint optimizer (SPO), and Electrostatic Discharge algorithm (EDA). The results of the hybrid models were compared via some criteria to choose the desired model. SPO had the most desirable performance when coupled with RF compared to other optimizers, exhibiting high R2 and low RMSE.