Computation of high-performance concrete compressive strength using standalone and ensembled machine learning techniques
Authors:
- Yue Xu,
- Waqas Ahmad,
- Ayaz Ahmad,
- Krzysztof Adam Ostrowski,
- Marta Dudek,
- Fahid Aslam,
- Panuwat Joyklad
Abstract
The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2 ), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.
- Record ID
- CUT3259e5ccf3474f5ea372c465f7095893
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2021
- Vol
- 14
- No
- 22
- Pages
- [1-16]
- Article number
- 7034
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 15-16; Bibliografia (liczba pozycji) - 46; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 14, Iss. 22, Spec. Iss.
- Substantive notes
- Special Issue: Emerging Construction Materials for Sustainable Infrastructure
- Keywords in English
- support vector regression, AdaBoost, random forest, machine learning, high-performance concrete
- DOI
- DOI:10.3390/ma14227034 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/14/22/7034 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Publication indicators
- Citation count
- 56
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT3259e5ccf3474f5ea372c465f7095893/
- URN
urn:pkr-prod:CUT3259e5ccf3474f5ea372c465f7095893
* 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.