Prediction of California Bearing Ratio Using Soil Index Properties by Regression and Machine-Learning Techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanics of Materials,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42947-022-00237-z.pdf
Reference67 articles.
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3. Chu, X., Dawson, A., & Thom, N. (2021). Prediction of resilient modulus with consistency index for fine-grained soils. Transportation Geotechnics, 31, 100650. https://doi.org/10.1016/j.trgeo.2021.100650
4. Taskiran, T. (2010). Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Advances in Engineering Software, 41, 886–892. https://doi.org/10.1016/j.advengsoft.2010.01.003
5. Cheng, Q., Tang, C. S., Zeng, H., Zhu, C., An, N., & Shi, B. (2020). Effects of microstructure on desiccation cracking of a compacted soil. Engineering Geology, 265, 105418. https://doi.org/10.1016/J.ENGGEO.2019.105418
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