Application of machine learning techniques to predict the unconfined compressive strength of sustainable cementitious materials used in the mining industry
- DOI
- 10.7939/r3-20sm-cx85
- Published
- 2023
- Container
- Not recorded
- Publisher
- University of Alberta Library
- Open access
- no
Credibility signals
limited evidence Score 43/100 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:10.7939/r3-20sm-cx85,
title = {Application of machine learning techniques to predict the unconfined compressive strength of sustainable cementitious materials used in the mining industry},
author = {Balasooriya Arachchilage, Chathuranga S J},
year = {2023},
doi = {10.7939/r3-20sm-cx85},
url = {https://doi.org/10.7939/r3-20sm-cx85}
}RIS
TY - JOUR TI - Application of machine learning techniques to predict the unconfined compressive strength of sustainable cementitious materials used in the mining industry AU - Balasooriya Arachchilage, Chathuranga S J PY - 2023 DO - 10.7939/r3-20sm-cx85 UR - https://doi.org/10.7939/r3-20sm-cx85 ER -
APA
J, B. A. C. S. (2023). Application of machine learning techniques to predict the unconfined compressive strength of sustainable cementitious materials used in the mining industry. https://doi.org/10.7939/r3-20sm-cx85
Source records
- datacite · retrieved 2026-09-25T15:11:07.731Z