Mix design of equal strength high volume fly ash concrete with artificial neural network
High volume fly ash concrete (HVFAC) has been widely used, and the mix proportion of HVFAC is usually designed based on the required compressive strength. Therefore, the research of HVFAC under the condition of equal strength has more engineering application value. However, in the current mix propor...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523004746 |
| _version_ | 1797454618103382016 |
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| author | Jikai Yao Senle Huang Yanwen Xu Chunping Gu Jintao Liu Yang Yang Tongyuan Ni Deyu Kong |
| author_facet | Jikai Yao Senle Huang Yanwen Xu Chunping Gu Jintao Liu Yang Yang Tongyuan Ni Deyu Kong |
| author_sort | Jikai Yao |
| collection | DOAJ |
| description | High volume fly ash concrete (HVFAC) has been widely used, and the mix proportion of HVFAC is usually designed based on the required compressive strength. Therefore, the research of HVFAC under the condition of equal strength has more engineering application value. However, in the current mix proportion design standard, there is no special discussion on the mix proportion design method of HVFAC. In this study, the compressive strength of HVFAC affected by four factors (water-binder ratio, FA content, curing method and curing age) was systematically studied. Based on 594 sets of strength data obtained from experiments, the method to determine the key mix proportion parameters (water-binder ratio and FA content) of equal strength HVFAC was proposed through machine learning algorithms (ML). Two ML methods (i.e., a multiple linear regression (MLR) and an artificial neural networks (ANN)) were developed to predict the HVFAC compressive strength and then their performances were compared. Through comparison, it was found that the mean absolute error and root mean squared error of the ANN model were both lower than the MLR model, and the ANN model has lower error and higher accuracy. In addition, the reliability of the proposed ANN model was verified with data in other literatures, the results showed the errors between the predicted values and the measured values are lower than 25%. The weight contribution rate of each factor to the strength was calculated. Among the four factors, the curing age had the greatest impact on the compressive strength, which contributes up to 60.3%. The influence degree of FA content is greater than that of water-binder ratio. With the development of curing age, the influence degree of FA content and water-binder ratio on the strength gradually decreases while the curing method increases. Finally, according to the ANN prediction results, the key mix parameters design method for equal strength HVFAC under multi-factor conditions was proposed, which provide guidance for concrete preparation in actual engineering. |
| first_indexed | 2024-03-09T15:39:44Z |
| format | Article |
| id | doaj.art-452227a2e3964c08ab17294496d51a6c |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-09T15:39:44Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-452227a2e3964c08ab17294496d51a6c2023-11-25T04:48:11ZengElsevierCase Studies in Construction Materials2214-50952023-12-0119e02294Mix design of equal strength high volume fly ash concrete with artificial neural networkJikai Yao0Senle Huang1Yanwen Xu2Chunping Gu3Jintao Liu4Yang Yang5Tongyuan Ni6Deyu Kong7College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China; Correspondence to: College of Civil Engineering, Zhejiang University of Technology, Liuhe Road 288, Hangzhou 310023, China.College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, ChinaHigh volume fly ash concrete (HVFAC) has been widely used, and the mix proportion of HVFAC is usually designed based on the required compressive strength. Therefore, the research of HVFAC under the condition of equal strength has more engineering application value. However, in the current mix proportion design standard, there is no special discussion on the mix proportion design method of HVFAC. In this study, the compressive strength of HVFAC affected by four factors (water-binder ratio, FA content, curing method and curing age) was systematically studied. Based on 594 sets of strength data obtained from experiments, the method to determine the key mix proportion parameters (water-binder ratio and FA content) of equal strength HVFAC was proposed through machine learning algorithms (ML). Two ML methods (i.e., a multiple linear regression (MLR) and an artificial neural networks (ANN)) were developed to predict the HVFAC compressive strength and then their performances were compared. Through comparison, it was found that the mean absolute error and root mean squared error of the ANN model were both lower than the MLR model, and the ANN model has lower error and higher accuracy. In addition, the reliability of the proposed ANN model was verified with data in other literatures, the results showed the errors between the predicted values and the measured values are lower than 25%. The weight contribution rate of each factor to the strength was calculated. Among the four factors, the curing age had the greatest impact on the compressive strength, which contributes up to 60.3%. The influence degree of FA content is greater than that of water-binder ratio. With the development of curing age, the influence degree of FA content and water-binder ratio on the strength gradually decreases while the curing method increases. Finally, according to the ANN prediction results, the key mix parameters design method for equal strength HVFAC under multi-factor conditions was proposed, which provide guidance for concrete preparation in actual engineering.http://www.sciencedirect.com/science/article/pii/S2214509523004746High volume fly ash concrete (HVFAC)Mix designEqual strengthMultiple Linear Regression (MLR)Artificial Neural Network (ANN) |
| spellingShingle | Jikai Yao Senle Huang Yanwen Xu Chunping Gu Jintao Liu Yang Yang Tongyuan Ni Deyu Kong Mix design of equal strength high volume fly ash concrete with artificial neural network Case Studies in Construction Materials High volume fly ash concrete (HVFAC) Mix design Equal strength Multiple Linear Regression (MLR) Artificial Neural Network (ANN) |
| title | Mix design of equal strength high volume fly ash concrete with artificial neural network |
| title_full | Mix design of equal strength high volume fly ash concrete with artificial neural network |
| title_fullStr | Mix design of equal strength high volume fly ash concrete with artificial neural network |
| title_full_unstemmed | Mix design of equal strength high volume fly ash concrete with artificial neural network |
| title_short | Mix design of equal strength high volume fly ash concrete with artificial neural network |
| title_sort | mix design of equal strength high volume fly ash concrete with artificial neural network |
| topic | High volume fly ash concrete (HVFAC) Mix design Equal strength Multiple Linear Regression (MLR) Artificial Neural Network (ANN) |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523004746 |
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