Researchers have released a large, open corpus of soil compaction tests, containing 2,854 laboratory compaction tests from six public sources across various energy levels and fines content. This dataset aims to overcome the limitations of existing correlations, which are often based on a small number of specimens from a single laboratory and compactive energy. The paper also presents machine learning models, including a tabular foundation model and symbolic regression, to predict soil density and water content, demonstrating their performance across different data splits and highlighting the conditional importance of compactive energy. AI
IMPACT Provides a valuable dataset and ML models for research in soil mechanics and civil engineering applications.
RANK_REASON The cluster contains an academic paper detailing a new dataset and machine learning baselines. [lever_c_demoted from research: ic=2 ai=0.4]
- modified-Proctor
- Proctor energy
- Proctor method
- Proctor test
- Sompote Youwai Dr.
- zero-air-voids
- foundation model
- laboratory compaction tests
- Symbolic regression
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