ArticleScientific reports2026
Unified model for prediction of the strength of soils stabilized with xanthan gum using white-box machine learning.
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
Biopolymer-based soil stabilization has attracted increasing attention as a sustainable approach in geotechnical engineering. However, accurate prediction of the compressive strength of biopolymer-stabilized soils remains challenging due to the complex interaction among soil properties, additive characteristics, and curing conditions. In this study, a comprehensive database comprising 248 unconfined compressive strength (UCS) tests on soils stabilized with xanthan gum was compiled and analyzed, and a unified and interpretable predictive framework applicable to a wide range of soil types and curing conditions was developed. To derive an explicit numerical relationship, a white-box machine learning algorithm based on the Group Method of Data Handling (GMDH) was employed to predict UCS. The input parameters included plasticity index (PI), xanthan gum content (XGC), curing time (CTi), curing temperature (CTe), initial moisture content (WMC), and the unconfined compressive strength of untreated soil (q
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