基于ANN的HVFAC拉伸性能预测评价

doi:10.11896/cldb.23070117

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Abstract The application of artificial neural networks (ANN) to predict the tensile properties of concrete is essential to study the understanding of concrete cracking mechanisms. Based on experimental data obtained for the compressive strength, ultimate tensile strain, tensile strength, and tensile modulus of elasticity of high-volume fly ash concrete (HVFAC), which varied in terms of fly ash admixture, aggregate-cement ratio, water-cement ratio, and curing age. This data was used to develop an artificial neural network (ANN) model to predict and evaluate the tensile properties of HVFAC using the RMSE minimum principle. The reliability of the prediction model was analyzed and evaluated with published literature data. The results show that the correlation coefficients between both predicted and experimental data are higher than 0.94, and the errors between the literature experimental and predicted values are within ±20% indicating that the established model has a high prediction accuracy. The weight analysis of ANN effects show that the aggregate-cement ratio has a greater effect on the compressive strength, ultimate tensile strain and tensile elastic modulus of HVFAC. Regarding the tensile properties of HVFAC, the water-cement ratio has a greater influence at the early stage. Howe-ver, with the development of age, the effect of fly ash admixture gradually increases and exceeds the effect of water-cement ratio.

Key words： artificial neural network fly ash concrete tensile strength modulus of elasticity ultimate tensile strain

Published: 25 May 2024 Online: 2024-05-28

CLC number:

TV42

Fund:Key Research and Development Program of Zhejiang Province(2021C01060).

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	Articles by authors
	NI Tongyuan
	DU Xin
	MO Yunbo
	HUANG Senle
	YANG Yang
	LIU Jintao

Cite this article:

NI Tongyuan,DU Xin,MO Yunbo, et al. Prediction and Evaluation of HVFAC Tensile Properties Based on ANN[J]. Materials Reports, 2024, 38(10): 23070117-9.

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http://www.mater-rep.com/EN/10.11896/cldb.23070117 OR http://www.mater-rep.com/EN/Y2024/V38/I10/23070117

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