Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/92003
PIRA download icon_1.1View/Download Full Text
Title: Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature
Authors: Ahmad, A
Ostrowski, KA
Maślak, M
Farooq, F
Mehmood, I 
Nafees, A
Issue Date: Aug-2021
Source: Materials, Aug 2021, v. 14, no. 15, 4222
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, respec-tively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, re-spectively. 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.
Keywords: Bagging
Compressive strength
Concrete
Decision tree
Gradient boosting
High temperature
Prediction
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Materials 
EISSN: 1996-1944
DOI: 10.3390/ma14154222
Rights: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The following publication Ahmad, A.; Ostrowski,K.A.; Ma´slak, M.; Farooq, F.;Mehmood, I.; Nafees, A. ComparativeStudy of Supervised MachineLearning Algorithms for Predictingthe Compressive Strength of Concreteat High Temperature. Materials 2021,14, 4222 is available at https://doi.org/10.3390/ma14154222
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
materials-14-04222-v2.pdf4.46 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

71
Last Week
3
Last month
Citations as of Mar 24, 2024

Downloads

35
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

87
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

71
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.