Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network
The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/3/1090 |
| _version_ | 1797623988198834176 |
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| author | Min Bai Zhe Zhang Kaiyue Cao Hui Li Cheng He |
| author_facet | Min Bai Zhe Zhang Kaiyue Cao Hui Li Cheng He |
| author_sort | Min Bai |
| collection | DOAJ |
| description | The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na<sub>2</sub>SiO<sub>3</sub> and the moles of Na<sub>2</sub>O and SiO<sub>2</sub> in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy. |
| first_indexed | 2024-03-11T09:36:31Z |
| format | Article |
| id | doaj.art-6cbb5a6e705f4b67a8f45b9d75296844 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T09:36:31Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-6cbb5a6e705f4b67a8f45b9d752968442023-11-16T17:17:00ZengMDPI AGMaterials1996-19442023-01-01163109010.3390/ma16031090Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural NetworkMin Bai0Zhe Zhang1Kaiyue Cao2Hui Li3Cheng He4School of Materials Science and Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Materials Science and Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Materials Science and Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Materials Science and Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Materials Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThe fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na<sub>2</sub>SiO<sub>3</sub> and the moles of Na<sub>2</sub>O and SiO<sub>2</sub> in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.https://www.mdpi.com/1996-1944/16/3/1090fly ash and slaggeopolymer pastecompressive strengthartificial neural network |
| spellingShingle | Min Bai Zhe Zhang Kaiyue Cao Hui Li Cheng He Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network Materials fly ash and slag geopolymer paste compressive strength artificial neural network |
| title | Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network |
| title_full | Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network |
| title_fullStr | Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network |
| title_full_unstemmed | Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network |
| title_short | Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network |
| title_sort | prediction of compressive strength of fly ash slag based geopolymer paste based on multi optimized artificial neural network |
| topic | fly ash and slag geopolymer paste compressive strength artificial neural network |
| url | https://www.mdpi.com/1996-1944/16/3/1090 |
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