Development of prediction model to predict the compressive strength of eco-friendly concrete using multivariate polynomial regression combined with stepwise method
Authors:
- Hamza Imran,
- Nadia Moneem Al-Abdaly,
- Mohammed Hammodi Shamsa,
- Amjed Shatnawi,
- Majed Ibrahim,
- Krzysztof Adam Ostrowski
Abstract
Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements.
- Record ID
- CUTe45af58b64f74c4781ed050a9de0f57c
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2022
- Vol
- 15
- No
- 1
- Pages
- [1-15]
- Article number
- 317
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 14-15; Bibliografia (liczba pozycji) - 34; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 15, Iss. 1, Spec. Iss.
- Substantive notes
- Special Issue: Artificial Intelligence for Cementitious Materials
- Keywords in English
- machine learning, compressive strength of concrete, ground granulated blast-furnace slag, recycled concrete aggregate, multivariate polynomial regression (MPR)
- ASJC Classification
- DOI
- DOI:10.3390/ma15010317 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/15/1/317 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe45af58b64f74c4781ed050a9de0f57c/
- URN
urn:pkr-prod:CUTe45af58b64f74c4781ed050a9de0f57c
* 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.