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This is a Quantitative Study study.

September 12, 2025Geotechnical ResearchOpen Access

Using machine learning paradigm to predict CBR value of treated expansive soil

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Authors

UAUmair Ahmad IJIrfan JamilSebelas Maret University HJH. JamalGeorgia Institute of Technology

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Implication

This analysis employs machine learning methods to forecast CBR values in expansive soil treated with hydrated-lime activated rice husk ash, highlighting the random forest model's efficacy.

Key Points

The random forest model demonstrated the best predictive performance for CBR values, outperforming other models.
Evaluations were based on statistical measures including mean absolute error and root mean square error.
Six soil parameters were used as inputs to predict the CBR value of hydrated-lime activated rice husk ash treated soil.
Sensitivity analysis revealed maximum dry density as the most critical factor influencing the CBR value.

Cite This Study

Ahmad et al. (2025) studied this question.

synapsesocial.com/papers/68d44a3731b076d99fa5365c https://doi.org/10.1680/jgere.24.00066

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Implication

This analysis employs machine learning methods to forecast CBR values in expansive soil treated with hydrated-lime activated rice husk ash, highlighting the random forest model's efficacy.

Using machine learning paradigm to predict CBR value of treated expansive soil

Discussion

Member takes

Implication

Key Points

Cite This Study

Using machine learning paradigm to predict CBR value of treated expansive soil

Discussion

Member takes

Implication

Key Points

Cite This Study

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