Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs.
- DOI
- 10.1038/s41598-025-15572-x
- Published
- 2025 Aug 29
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-025-15572-x,
title = {Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs.},
author = {Ali M and Aziz M and Ali A and Ali U},
year = {2025},
journal = {Scientific reports},
doi = {10.1038/s41598-025-15572-x},
url = {https://doi.org/10.1038/s41598-025-15572-x}
}RIS
TY - JOUR TI - Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs. AU - Ali M AU - Aziz M AU - Ali A AU - Ali U PY - 2025 JO - Scientific reports DO - 10.1038/s41598-025-15572-x UR - https://doi.org/10.1038/s41598-025-15572-x ER -
APA
M, A., M, A., A, A., & U, A. (2025). Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs.. Scientific reports. https://doi.org/10.1038/s41598-025-15572-x
Source records
- pubmed · retrieved 2026-09-26T17:39:15.235Z