Predicting California Bearing Ratio Using Hybrid Least Square Support Vector Regression with IAOA, ESMA, and RKO MetaHeuristic Algorithms
Abstract
The California Bearing Ratio (CBR) test is a crucial geotechnical parameter for evaluating soil strength. This study proposes a Least Squares Support Vector Regression (LSSVR) model to predict CBR values using compaction characteristics, moisture content, and soil properties. A dataset comprising 110 soil samples was used, with 70% for training and 30% for testing. To enhance predictive accuracy, three metaheuristic algorithms—Improved Arithmetic Optimization Algorithm (IAOA), Equilibrium Slime Mould Algorithm (ESMA), and Runge Kutta Optimization (RKO)—were integrated with LSSVR, forming hybrid models LSIA, LSEM, and LSRK. These algorithms optimized the regularization parameter (C) and kernel parameter (Gamma) to improve model generalization. Performance evaluation using R², RMSE, and MAE showed that the LSIA model outperformed all others, achieving an R² of 0.9975 (training) and 0.9932 (testing), along with the lowest RMSE (0.5489) and MAE (0.3176). The results confirm that LSIA exhibits superior predictive accuracy and robustness, making it a reliable and time-efficient alternative for geotechnical applications.
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DOI: https://doi.org/10.31449/inf.v49i25.7709
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