Prediction and Evaluation of HVFAC Tensile Properties Based on ANN
NI Tongyuan1,2, DU Xin1, MO Yunbo3,*, HUANG Senle1, YANG Yang1,2, LIU Jintao1,2
1 College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China 2 Zhejiang Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology, Hangzhou 310023, China 3 Zhejiang Infrastructure Construction Group Co., Ltd., Hangzhou 310012, China
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.
作者简介: 倪彤元,浙江工业大学高级工程师、 硕士研究生导师。目前主要研究领域为混凝土结构变形与裂缝监测及防治、工业废弃物资源化综合利用等。发表论文 40 余篇,包括 Construction and Building Materials、Measurement 等。
引用本文:
倪彤元, 杜鑫, 莫云波, 黄森乐, 杨杨, 刘金涛. 基于ANN的HVFAC拉伸性能预测评价[J]. 材料导报, 2024, 38(10): 23070117-9.
NI Tongyuan, DU Xin, MO Yunbo, HUANG Senle, YANG Yang, LIU Jintao. Prediction and Evaluation of HVFAC Tensile Properties Based on ANN. Materials Reports, 2024, 38(10): 23070117-9.
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