Integrating Atterberg Limits with Machine Learning for Screening-Scale Prediction of Volumetric Behavior in Pathum Thani Clay Soils

Rattanachot Thongpong; Pattaraporn Nueasri

doi:10.55003/ETH.420408

Authors

Rattanachot Thongpong Faculty of Industrial Technology, Valaya Alongkorn Rajabhat University under the Royal Patronage, Thailand
Pattaraporn Nueasri Faculty of Industrial Technology, Valaya Alongkorn Rajabhat University under the Royal Patronage, Thailand

DOI:

https://doi.org/10.55003/ETH.420408

Keywords:

Atterberg Limits, Polynomial regression, Random Forest, SVR, Volumetric prediction

Abstract

This study investigates the predictive relationship between Atterberg limits and the volumetric ratio behavior (VLL, VPL, and VSL) of tropical clay soils. Laboratory testing, following ASTM D4318, was conducted on 50 clay samples collected from Pathum Thani Province, Thailand. Estimated volumetric ratios were derived from mass–moisture–density relationships representing liquid, plastic, and shrinkage states. Linear, polynomial, and machine learning models, including Random Forest and Support Vector Regression (SVR), were developed to evaluate the statistical association between index parameters and volume change behavior. The models showed weak-to-moderate correlation (R² = 0.31–0.55), indicating that the derived relationships can provide qualitative insights rather than quantitative predictions, supporting a conceptual understanding of soil volume behavior. The Shrinkage Limit (SL) consistently emerged as the most influential parameter, reflecting its strong association with moisture-induced volume reduction and soil–water interaction mechanisms. The results suggest that Atterberg limits can serve as qualitative indicators of volumetric change potential rather than quantitative predictors. Although the models exhibited low explanatory power, they provide transparent, reproducible insights into how index-based soil properties correspond to volumetric transitions. This framework supports early-stage and cost-effective assessment of expansive soils, offering a practical foundation for identifying shrink–swell tendencies before advanced testing. The approach contributes to improving preliminary geotechnical evaluation practices in tropical environments and establishes a reference for future validation incorporating mineralogical and suction-related parameters.

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