ArticleScientific reports2026
Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Shear strength of soil plays an essential role in geotechnical properties affecting construction stability. Conventional laboratory testing for determining soil shear strength is often expensive and time-consuming. Therefore, machine learning (ML) methods were employed to predict soil shear strength using geotechnical parameters compiled from previously published literature based on investigations conducted at the Le Trong Tan Geleximco project in western Hanoi, Vietnam. In total, there were 202 samples analyzed, including parameters such as depth, sand percent, loam percent, clay percent, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plasticity index, and liquidity index. Four predictive models have been developed and analyzed, including Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Multi-Expression Programming (MEP). The model performance was evaluated using several parameters like coefficient of determination (R
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