A Comparative Analysis of Machine Learning Models for Predicting Loess Collapse Potential
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geology,Soil Science,Geotechnical Engineering and Engineering Geology,Architecture
Link
https://link.springer.com/content/pdf/10.1007/s10706-023-02593-4.pdf
Reference65 articles.
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2. Al-Harthi AA, Bankher KA (1999) Collapsing loess-like soil in western Saudi Arabia. J Arid Environ 41(4):383–399. https://doi.org/10.1006/jare.1999.0494
3. Assallay AM, Rogers CDF, Smalley IJ (1996) Engineering properties of loess in Libya. J Arid Environ 32(4):373–386. https://doi.org/10.1006/jare.1996.0031
4. ASTM, D5333 (1996) Standard test method for measurement of collapse potential of soils. ASTM International. https://doi.org/10.1520/D5333-92R96.
5. Basma AA, Kallas N (2004) Modeling soil collapse by artificial neural networks. Geotech Geol Eng 22(3):427–438. https://doi.org/10.1023/B:GEGE.0000025044.72718.db
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