Integrating experimental data and machine learning to predict collapse potential of loessic and lacustrine-alluvial soils using suction and SWCC parameters.
- DOI
- 10.1038/s41598-026-46537-3
- Published
- 2026 May 11
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-46537-3,
title = {Integrating experimental data and machine learning to predict collapse potential of loessic and lacustrine-alluvial soils using suction and SWCC parameters.},
author = {Tabatabaei AA and Abtahi SM and Hashemolhosseini H and Hajiannia A},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-46537-3},
url = {https://doi.org/10.1038/s41598-026-46537-3}
}RIS
TY - JOUR TI - Integrating experimental data and machine learning to predict collapse potential of loessic and lacustrine-alluvial soils using suction and SWCC parameters. AU - Tabatabaei AA AU - Abtahi SM AU - Hashemolhosseini H AU - Hajiannia A PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-46537-3 UR - https://doi.org/10.1038/s41598-026-46537-3 ER -
APA
AA, T., SM, A., H, H., & A, H. (2026). Integrating experimental data and machine learning to predict collapse potential of loessic and lacustrine-alluvial soils using suction and SWCC parameters.. Scientific reports. https://doi.org/10.1038/s41598-026-46537-3
Source records
- pubmed · retrieved 2026-09-25T19:15:17.645Z