Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm
Authors:
- Ayaz Ahmad,
- Furqan Farooq,
- Pawel Niewiadomski,
- Krzysztof Ostrowski,
- Arslan Akbar,
- Fahid Aslam,
- Rayed Alyousef
Abstract
Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 submodels to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2 , MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.
- Record ID
- CUT7acb3fb6637a4eb3837c0ccfdabc76a5
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2021
- Vol
- 14
- No
- 4
- Pages
- [1-21]
- Article number
- 794
- Other elements of collation
- rys.; schem.; tab.; wykr.; Bibliografia (na s.) - 19-21; Bibliografia (liczba pozycji) - 48; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 14, Iss. 4, Spec. Iss.
- Substantive notes
- Special Issue: Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models
- Keywords in English
- concrete compressive strength, fly ash waste, ensemble modeling, decision tree, DT-bagging regression, cross-validation python
- DOI
- DOI:10.3390/ma14040794 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/14/4/794 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT7acb3fb6637a4eb3837c0ccfdabc76a5/
- URN
urn:pkr-prod:CUT7acb3fb6637a4eb3837c0ccfdabc76a5
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.