Analyzing the compressive strength of ceramic waste-based concrete using experiment and Artificial Neural Network (ANN) approach
Authors:
- Hongwei Song,
- Ayaz Ahmad,
- Krzysztof Adam Ostrowski,
- Marta Dudek
Abstract
In a fast-growing population of the world and regarding meeting consumer’s requirements, solid waste landfills will continue receiving a substantial amount of waste. The utilization of solid waste materials in concrete has gained the attention of the researchers. Ceramic waste powder (CWP) is considered to be one of the most harmful wastes for the environment, which may cause water, soil, and air pollution. The aim of this study was comprised of two phases. Phase one was based on the characterization of CWP with respect to its composition, material testing (coarse aggregate, fine aggregate, cement,) and evaluation of concrete properties both in fresh and hardened states (slump, 28 days compressive strength, and dry density). Concrete mixes were prepared in order to evaluate the compressive strength (CS) of the control mix, with partial replacement of the cement with CWP of 10 and 20% by mass of cement and 60 prepared mixes. However, phase two was based on the application of the artificial neural network (ANN) and decision tree (DT) approaches, which were used to predict the CS of concrete. The linear coefficient correlation (R2) value from the ANN model indicates better performance of the model. Moreover, the statistical check and k-fold cross validation methods were also applied for the performance confirmation of the model. The mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to confirm the model’s precision.
- Record ID
- CUTa702feb97eaf43ef8ee36db49c7f5aae
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2021
- Vol
- 14
- No
- 16
- Pages
- [1-17]
- Article number
- 4518
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 16-17; Bibliografia (liczba pozycji) - 50; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 14, Iss. 16, Spec. Iss.
- Substantive notes
- Special Issue: Advances in Cement, Lime and Concrete
- Keywords in English
- ceramic waste powder, concrete, cement, artificial neural network, prediction, machine learning algorithms
- DOI
- DOI:10.3390/ma14164518 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/14/16/4518 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Publication indicators
- Citation count
- 52
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTa702feb97eaf43ef8ee36db49c7f5aae/
- URN
urn:pkr-prod:CUTa702feb97eaf43ef8ee36db49c7f5aae
* 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.