Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/7/2400 |
| _version_ | 1797438668182388736 |
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| author | Rongchuan Cao Zheng Fang Man Jin Yu Shang |
| author_facet | Rongchuan Cao Zheng Fang Man Jin Yu Shang |
| author_sort | Rongchuan Cao |
| collection | DOAJ |
| description | Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R<sup>2</sup>) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R<sup>2</sup> value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers. |
| first_indexed | 2024-03-09T11:41:36Z |
| format | Article |
| id | doaj.art-860baecff4ce402493cb38a417569e71 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T11:41:36Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-860baecff4ce402493cb38a417569e712023-11-30T23:31:38ZengMDPI AGMaterials1996-19442022-03-01157240010.3390/ma15072400Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer ConcreteRongchuan Cao0Zheng Fang1Man Jin2Yu Shang3School of Civil Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Civil Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Civil Engineering and Architecture, Henan University, Kaifeng 475000, ChinaSchool of Civil Engineering, Wuhan University, Wuhan 430072, ChinaGeopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R<sup>2</sup>) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R<sup>2</sup> value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.https://www.mdpi.com/1996-1944/15/7/2400geopolymerfly ashconcretemodelingmachine learning |
| spellingShingle | Rongchuan Cao Zheng Fang Man Jin Yu Shang Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete Materials geopolymer fly ash concrete modeling machine learning |
| title | Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete |
| title_full | Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete |
| title_fullStr | Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete |
| title_full_unstemmed | Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete |
| title_short | Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete |
| title_sort | application of machine learning approaches to predict the strength property of geopolymer concrete |
| topic | geopolymer fly ash concrete modeling machine learning |
| url | https://www.mdpi.com/1996-1944/15/7/2400 |
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