Simulation of depth of wear of eco-friendly concrete using machine learning based computational approaches
Authors:
- Mohsin Ali Khan,
- Furqan Farooq,
- Mohammad Faisal Javed,
- Adeel Zafar,
- Krzysztof Adam Ostrowski,
- Fahid Aslam,
- Seweryn Malazdrewicz,
- Mariusz Maślak
Abstract
To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.
- Record ID
- CUT7078b728539f4cf4a081940544210346
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2022
- Vol
- 15
- No
- 1
- Pages
- [1-28]
- Article number
- 58
- Other elements of collation
- rys.; schem.; tab.; wykr.; Bibliografia (na s.) - 25-28; Bibliografia (liczba pozycji) - 89; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 15, Iss. 1, Spec. Iss.
- Substantive notes
- Special Issue: Artificial Intelligence for Cementitious Materials
- Keywords in English
- fly-ash, depth of wear (DW), abrasion resistance, artificial intelligence (AI), random forest regression (RFR), gene expression programming (GEP)
- ASJC Classification
- DOI
- DOI:10.3390/ma15010058 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/15/1/58/htm 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/CUT7078b728539f4cf4a081940544210346/
- URN
urn:pkr-prod:CUT7078b728539f4cf4a081940544210346
* 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.