Experimental assessment with data-driven machine learning-based prediction of compressive strength of waste natural fiber-reinforced sustainable concrete
Prince Paul, Md Kawsarul Islam Kabbo, Rajarshi Das Gupta, Md Habibur Rahman Sobuz, Prosanjit Baral, Mohammed Jameel, Sani Aliyu Abubakar · 2025
Read Experimental assessment with data-driven machine learning-based prediction of compressive strength of waste natural fiber-reinforced sustainable concrete by Prince Paul, Md Kawsarul Islam Kabbo, Rajarshi Das Gupta, Md Habibur Rahman Sobuz, Prosanjit Baral, Mohammed Jameel, Sani Aliyu Abubakar (2025) 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-12820310.txt.
Description
Prince Paul and co-authors published a study in 2025 on concrete reinforced with waste natural fibers, such as jute, coir from coconut husks, and bamboo. The researchers wanted to know how strong this sustainable concrete is under compression, and whether machine learning models can predict that strength from the mixture design.
They compiled 444 concrete mix designs from the literature, each described by seven input variables. Six machine learning algorithms were trained and tuned on the data. A gradient boosting model performed best, with the strongest fit and the lowest prediction error. Analyses of which variables drove the predictions named cement content, water, and coarse aggregate as the dominant factors. Fiber content and curing time played a smaller but real part. To test the modeling, the team cast ten fresh mixes with coir fiber. A fiber share near three quarters of one percent gave the best strength improvement.
Catalog record
| CREATOR | Prince Paul, Md Kawsarul Islam Kabbo, Rajarshi Das Gupta, Md Habibur Rahman Sobuz, Prosanjit Baral, Mohammed Jameel, Sani Aliyu Abubakar |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 69 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |