Estimation of California Bearing Ratio of soils using Random Forest based machine learning
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In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory.
Nội dung trích xuất từ tài liệu:
Estimation of California Bearing Ratio of soils using Random Forest based machine learning
Nội dung trích xuất từ tài liệu:
Estimation of California Bearing Ratio of soils using Random Forest based machine learning
Tìm kiếm theo từ khóa liên quan:
Science and transport technology Bearing ratio Random Forest Machine learning Maximum Dry Density Gravel contentGợi ý tài liệu liên quan:
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