Comparative study of supervised machine larning agorithms for predicting the compressive strength of concrete at high temperature
Authors:
- Ayaz Ahmad,
- Krzysztof Adam Ostrowski,
- Mariusz Maślak,
- Furqan Farooq,
- Imran Mehmood,
- Afnan Nafees
Abstract
High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.
- Record ID
- CUT1bfca3538a444643b365253bc53a0157
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2021
- Vol
- 14
- No
- 15
- Pages
- [1-19]
- Article number
- 4222
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 16-19; Bibliografia (liczba pozycji) - 77; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 14, Iss. 15, Spec. Iss.
- Substantive notes
- Special Issue: Artificial Intelligence for Cementitious Materials
- Keywords in English
- concrete, compressive strength, high temperature, prediction, decision tree, bagging, gradient boosting
- DOI
- DOI:10.3390/ma14154222 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/14/15/4222/htm Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Publication indicators
- = 91
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT1bfca3538a444643b365253bc53a0157/
- URN
urn:pkr-prod:CUT1bfca3538a444643b365253bc53a0157
* 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.