An explainable ensemble machine learning approach for coefficient of compressibility prediction of Chengdu clay
Researchers have developed an explainable machine learning framework to predict the coefficient of compressibility for Chengdu clay in geotechnical engineering. By using hybrid optimization and SHAP analysis, the study identifies water content and liquidity index as the primary factors influencing soil behavior.
Why it matters
This predictive model improves the accuracy and safety of geotechnical engineering projects by providing reliable risk assessments for regional soil types.
Scientific Reports ( 2026 ) Cite this article
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