The characterization of soil resistance properties is of paramount importance in the realm of pavement design. Indices such as dynamic modulus, resilient modulus, and California Bearing Ratio (CBR) are commonly employed to ascertain these properties. The CBR test is an experimental methodology implemented in laboratory or field settings to assess the subgrade's shear strength and stiffness modulus. Due to the exorbitant expenses linked with these assessments, it is deemed essential to implement distinct methodologies to predict their results. The advancement of artificial intelligence (AI) methodologies has enabled the production of innovative models and algorithms. Implementing these techniques enables research professionals to choose predictive methodologies over empirical ones. This study employs an AI methodology to assess the mechanical properties of the CBR index. The radial basis function (RBF) technique was used as a problem-solving methodology to create a machine-learning model. The methodology entails the utilization of sampling experiments to tackle a precisely defined problem. Three different meta-heuristic optimisation techniques were used in this study, namely, the Archimedes optimization algorithm (AOA), bald eagle search optimization (BESO), and Ebola optimization search (EOS), to achieve optimal outcomes. Furthermore, the entire procedure is subjected to evaluation by employing evaluators. According to the results, RBEO obtained the most suitable values in presented metrics, such as R2 and RMSE, equal to 0.9969 and 0.629, respectively. In general, EOS indicated the most desirable accuracy when coupled with RBF compared to BESO and AOA.
来源平台：MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN
