Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms
- DOI
- 10.1016/j.cscm.2023.e01893
- Published
- 2023-07
- Container
- Case Studies in Construction Materials
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.cscm.2023.e01893,
title = {Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms},
author = {Balahaha Hadi Ziyad Sami and Balahaha Fadi Ziyad Sami and Pavitra Kumar and Ali Najah Ahmed and Goodnews E. Amieghemen and Muhammad M. Sherif and Ahmed El-Shafie},
year = {2023},
journal = {Case Studies in Construction Materials},
doi = {10.1016/j.cscm.2023.e01893},
url = {https://doi.org/10.1016/j.cscm.2023.e01893}
}RIS
TY - JOUR TI - Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms AU - Balahaha Hadi Ziyad Sami AU - Balahaha Fadi Ziyad Sami AU - Pavitra Kumar AU - Ali Najah Ahmed AU - Goodnews E. Amieghemen AU - Muhammad M. Sherif AU - Ahmed El-Shafie PY - 2023 JO - Case Studies in Construction Materials DO - 10.1016/j.cscm.2023.e01893 UR - https://doi.org/10.1016/j.cscm.2023.e01893 ER -
APA
Sami, B. H. Z., Sami, B. F. Z., Kumar, P., Ahmed, A. N., Amieghemen, G. E., Sherif, M. M., & El-Shafie, A. (2023). Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms. Case Studies in Construction Materials. https://doi.org/10.1016/j.cscm.2023.e01893
Source records
- crossref · retrieved 2026-09-25T14:58:09.184Z