Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model
This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Polymers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4360/14/7/1423 |
| _version_ | 1797437891090055168 |
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| author | Ali Abdulhasan Khalaf Katalin Kopecskó Ildiko Merta |
| author_facet | Ali Abdulhasan Khalaf Katalin Kopecskó Ildiko Merta |
| author_sort | Ali Abdulhasan Khalaf |
| collection | DOAJ |
| description | This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the training and testing data, respectively. Based on the optimised model, a powerful design chart for determining the mix-design parameters of FA geopolymer concretes was generated. It is applicable for both one- and two-part geopolymer concretes, as it takes a wide range of mix-design parameters into account. The design chart (with its relatively small error) will ensure cost- and time-efficient geopolymer production in future applications. |
| first_indexed | 2024-03-09T11:29:12Z |
| format | Article |
| id | doaj.art-180a5d88138c47a892b0f3c17e384bb8 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-09T11:29:12Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-180a5d88138c47a892b0f3c17e384bb82023-11-30T23:54:13ZengMDPI AGPolymers2073-43602022-03-01147142310.3390/polym14071423Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network ModelAli Abdulhasan Khalaf0Katalin Kopecskó1Ildiko Merta2Department of Engineering Geology and Geotechnics, Faculty of Civil Engineering, Budapest University of Technology and Economics, 1111 Budapest, HungaryDepartment of Engineering Geology and Geotechnics, Faculty of Civil Engineering, Budapest University of Technology and Economics, 1111 Budapest, HungaryBuilding Physics and Building Ecology, Institute of Material Technology, Faculty of Civil Engineering, TU Wien, 1040 Vienna, AustriaThis article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the training and testing data, respectively. Based on the optimised model, a powerful design chart for determining the mix-design parameters of FA geopolymer concretes was generated. It is applicable for both one- and two-part geopolymer concretes, as it takes a wide range of mix-design parameters into account. The design chart (with its relatively small error) will ensure cost- and time-efficient geopolymer production in future applications.https://www.mdpi.com/2073-4360/14/7/1423fly ashgeopolymer concretealkali-activated bindercompressive strengthfeedforward layered neural networkMatlab code |
| spellingShingle | Ali Abdulhasan Khalaf Katalin Kopecskó Ildiko Merta Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model Polymers fly ash geopolymer concrete alkali-activated binder compressive strength feedforward layered neural network Matlab code |
| title | Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model |
| title_full | Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model |
| title_fullStr | Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model |
| title_full_unstemmed | Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model |
| title_short | Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model |
| title_sort | prediction of the compressive strength of fly ash geopolymer concrete by an optimised neural network model |
| topic | fly ash geopolymer concrete alkali-activated binder compressive strength feedforward layered neural network Matlab code |
| url | https://www.mdpi.com/2073-4360/14/7/1423 |
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