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Optimization-Enhanced Support Vector Machine Algorithms for California Bearing Ratio Prediction of Lateritic Soils: A Comparative Analysis | Synapse
March 3, 2026
Optimization-Enhanced Support Vector Machine Algorithms for California Bearing Ratio Prediction of Lateritic Soils: A Comparative Analysis
PV
Pranshu Vardhan
SK
Suneet Kaur
Puntos clave
California Bearing Ratio is effectively predicted using optimization-enhanced support vector machine algorithms, resulting in improved accuracy.
The study compares various predictive modeling techniques to determine the most effective approach for lateritic soils.
Results indicate a significant enhancement in prediction accuracy with optimized support vector machine algorithms over traditional methods.
Findings highlight the importance of advanced modeling techniques in soil engineering, especially for infrastructure applications.
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Vardhan et al. (Sat,) studied this question.
synapsesocial.com/papers/69a75a1cc6e9836116a1fa99
https://doi.org/https://doi.org/10.1007/s40009-026-01948-8