Peer-reviewedCC BY 4.0Scientific Reports
Machine learning-driven reverse prediction of concrete mix design: a comparative study of twelve algorithms
Related
- Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance
- Machine learning-based prediction of compressive strength of wastewater-exposed concrete using explainable and optimized models
- Interpretable machine learning prediction of interlayer bond strength in self-consolidating concrete using multiple bond test methods
- Machine learning-based prediction of 90-day tensile strength in geopolymer concrete under varying input-variable scenarios
- Interpretable Machine Learning for Compressive Strength Prediction of GGBS–Fly Ash Blended Concrete: Generalization Across Training-Set Compositions and Feature-Attribution Sensitivity
BibTeX
@article{nadimalla2026machine,
title = {{Machine learning-driven reverse prediction of concrete mix design: a comparative study of twelve algorithms}},
author = {Altamashuddinkhan Nadimalla and Md Azree Othuman Mydin and Siti Aliyyah Masjuki and Afiya Abdul Sattar and Paul O. Awoyera and Olaolu George Fadugba},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-77809-7},
url = {https://www.nature.com/articles/s41598-026-77809-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine learning-driven reverse prediction of concrete mix design: a comparative study of twelve algorithms
AU - Altamashuddinkhan Nadimalla
AU - Md Azree Othuman Mydin
AU - Siti Aliyyah Masjuki
AU - Afiya Abdul Sattar
AU - Paul O. Awoyera
AU - Olaolu George Fadugba
PY - 2026
DA - 2026/11/04
JO - Scientific Reports
DO - 10.1038/s41598-026-77809-7
UR - https://www.nature.com/articles/s41598-026-77809-7
SN - 2045-2322
ER -