Optimization-Enhanced Support Vector Machine Algorithms for California Bearing Ratio Prediction of Lateritic Soils: A Comparative Analysis

Pranshu Vardhan; Suneet Kaur

doi:10.1007/s40009-026-01948-8

January 17, 2026National Academy Science Letters

Optimization-Enhanced Support Vector Machine Algorithms for California Bearing Ratio Prediction of Lateritic Soils: A Comparative Analysis

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Key Points

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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Implication

Comparative analysis reveals enhanced predictive modeling of California Bearing Ratio in lateritic soils, suggesting improved soil assessment methods.

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Vardhan et al. (2026) studied this question.

synapsesocial.com/papers/69a75a1cc6e9836116a1fa99 https://doi.org/10.1007/s40009-026-01948-8

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Optimization-Enhanced Support Vector Machine Algorithms for California Bearing Ratio Prediction of Lateritic Soils: A Comparative Analysis

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Optimization-Enhanced Support Vector Machine Algorithms for California Bearing Ratio Prediction of Lateritic Soils: A Comparative Analysis

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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