Abstract
Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ([Formula: see text]) and sleeve friction ([Formula: see text]) as input variables. Pearson correlation and sensitivity analysis confirmed that these variables are highly correlated with the classification results. To enhance classification performance, 25 optimization algorithms were applied, and the models were validated against an independent dataset of 208 CPT records. The results revealed that 23 of the algorithms successfully improved the SVM classification accuracy. Among these, 18 algorithms achieved higher accuracy than the current engineering standard, the "Code for in-situ Measurement of Railway Engineering Geology." Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. Moreover, the models were thoroughly evaluated using Monte Carlo simulations, confusion matrices, ROC curves, and 10 key performance metrics. In conclusion, integrating evolutionary algorithms with SVM for soil classification offers a promising approach to enhancing the efficiency and accuracy of soil analysis in engineering applications.