Mix design of equal strength high volume fly ash concrete with artificial neural network

Jikai Yao; Senle Huang; Yanwen Xu; Chunping Gu; Jintao Liu; Yang Yang; Tongyuan Ni; Deyu Kong

Case Studies in Construction Materials (Dec 2023)

Mix design of equal strength high volume fly ash concrete with artificial neural network

Jikai Yao,
Senle Huang,
Yanwen Xu,
Chunping Gu,
Jintao Liu,
Yang Yang,
Tongyuan Ni,
Deyu Kong

Affiliations

Jikai Yao: 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, China
Senle Huang: 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, China
Yanwen Xu: 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, China
Chunping Gu: 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, China; Correspondence to: College of Civil Engineering, Zhejiang University of Technology, Liuhe Road 288, Hangzhou 310023, China.
Jintao Liu: 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, China
Yang Yang: 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, China
Tongyuan Ni: 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, China
Deyu Kong: 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, China

Journal volume & issue: Vol. 19
p. e02294

Abstract

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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.

Published in Case Studies in Construction Materials

ISSN: 2214-5095 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
Website: https://www.journals.elsevier.com/case-studies-in-construction-materials

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