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
- 2026-05-11
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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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 = {Azam Al-Sadat Tabatabaei and Sayyed Mahdi Abtahi and Hamid Hashemolhosseini and Alborz Hajiannia},
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 - Azam Al-Sadat Tabatabaei AU - Sayyed Mahdi Abtahi AU - Hamid Hashemolhosseini AU - Alborz Hajiannia PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-46537-3 UR - https://doi.org/10.1038/s41598-026-46537-3 ER -
APA
Tabatabaei, A. A., Abtahi, S. M., Hashemolhosseini, H., & Hajiannia, A. (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
- crossref · retrieved 2026-11-10T18:49:16.928Z