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Journal of Zhejiang University SCIENCE A 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/jzus.A2000379


An artificial neural network model of compressive behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder


Author(s):  Fang-yu LIU, Wen-qi DING, Ya-fei QIAO, Lin-bing WANG, Qi-yang CHEN

Affiliation(s):  Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, 24061 Virginia, USA; more

Corresponding email(s):   yafei.qiao@tongji.edu.cn, wangl@vt.edu

Key Words:  Artificial neural network, Hybrid fiber reinforced concrete, Compressive behavior, Modeling, Experimental data, Stress-strain curve


Fang-yu LIU, Wen-qi DING, Ya-fei QIAO, Lin-bing WANG, Qi-yang CHEN. An artificial neural network model of compressive behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .

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author="Fang-yu LIU, Wen-qi DING, Ya-fei QIAO, Lin-bing WANG, Qi-yang CHEN",
journal="Journal of Zhejiang University Science A",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000379"
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%T An artificial neural network model of compressive behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder
%A Fang-yu LIU
%A Wen-qi DING
%A Ya-fei QIAO
%A Lin-bing WANG
%A Qi-yang CHEN
%J Journal of Zhejiang University SCIENCE A
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%D 1998
%I Zhejiang University Press & Springer
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T1 - An artificial neural network model of compressive behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder
A1 - Fang-yu LIU
A1 - Wen-qi DING
A1 - Ya-fei QIAO
A1 - Lin-bing WANG
A1 - Qi-yang CHEN
J0 - Journal of Zhejiang University Science A
VL - -1
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Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000379


Abstract: 
modeling the mechanical behavior of hybrid fiber reinforced concrete (HFRC), a composite material, is crucial for the design of HFRC and HFRC structures. In this study, a new approach is proposed to model the compression behavior of HFRC by using an artificial neural network (ANN) method. The proposed ANN model incorporates two new developments: the prediction of the compressive stress-strain curve and consideration of 23 features of components of HFRC. To build a database for the ANN model, a series of compression experiments were performed on hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder, with a focus on the fiber content/ratio effect. Relevant published data were also collected. Three indices were used to train and evaluate the ANN model. To highlight the performance of the ANN model, it was compared with a traditional equation-based model. The results revealed that the relative errors of the predicted compressive strength and strain corresponding to compressive strength of the ANN model were close to 0, while the corresponding values from the equation-based model were higher. Therefore, the ANN model is better able to consider the effect of different components on the compressive behavior of HFRC in terms of compressive strength, the strain corresponding to compressive strength, and the compressive stress-strain curve. Such an ANN model could also be a good tool to predict the mechanical behavior of other composite materials.

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