Predicting CBR values using gaussian process regression and meta-heuristic algorithms in geotechnical engineering
Author:
Funder
This work was supported by Natural Science Foundation Project of Nantong City in 2023.
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41939-024-00428-0.pdf
Reference35 articles.
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3. Alzabeebee S, Mohamad SA, Al-Hamd RKS (2022) Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve. Road Mater Pavement Design 23(12):2733–2750
4. Bardhan A, Gokceoglu C, Burman A, Samui P, Asteris PG (2021) Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions. Eng Geol 291:106239
5. Bhatt S, Jain PK, Pradesh M (2014) Prediction of California bearing ratio of soils using artificial neural network. Am Int J Res Sci Technol Eng Math 8:156–161
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