Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete
Md Alhaz Uddin, Md Habibur Rahman Sobuz, Md Kawsarul Islam Kabbo, Ibrahim Y Hakeem, Ratan Lal, Ali Masria, Sani Aliyu Abubakar · 2026
Read Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete by Md Alhaz Uddin, Md Habibur Rahman Sobuz, Md Kawsarul Islam Kabbo, Ibrahim Y Hakeem, Ratan Lal, Ali Masria, Sani Aliyu Abubakar (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13377197.txt.
Description
Md Alhaz Uddin and co-authors use machine learning to predict the strength of concrete mixed with steel slag in a 2026 study. Electric arc furnaces leave mountains of slag, and reusing it in concrete eases a growing waste problem. Slag's odd physical traits make strength hard to guess, so the team built models instead of running endless lab batches.
They assembled 730 samples with mix details and measured strength. Five models were tested, including hybrids that stack two methods. A boosting model scored highest on test data, with a hybrid close behind. Explanation tools showed curing time and cement content drive strength most. The authors say such models can cut costly trial mixes and support greener concrete design.
Catalog record
| CREATOR | Md Alhaz Uddin, Md Habibur Rahman Sobuz, Md Kawsarul Islam Kabbo, Ibrahim Y Hakeem, Ratan Lal, Ali Masria, Sani Aliyu Abubakar |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |