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CLC number: TU528.572

On-line Access: 2021-06-16

Received: 2020-08-23

Revision Accepted: 2020-11-11

Crosschecked: 2021-09-03

Cited: 0

Clicked: 2950

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ya-fei Qiao

https://orcid.org/0000-0001-7567-3988

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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.9 P.721-735

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


Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder: experiments and an artificial neural network model


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, Virginia 24061, USA; more

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

Key Words:  Experiments, Artificial neural network (ANN), Hybrid fiber-reinforced concrete (HFRC), Compressive behavior, Stress-strain curve


Fang-yu Liu, Wen-qi Ding, Ya-fei Qiao, Lin-bing Wang, Qi-yang Chen. Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder: experiments and an artificial neural network model[J]. Journal of Zhejiang University Science A, 2021, 22(9): 721-735.

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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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pages="721-735",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000379"
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%T Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder: experiments and an artificial neural network model
%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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%P 721-735
%@ 1673-565X
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000379

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T1 - Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder: experiments and an artificial neural network model
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 - 22
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SP - 721
EP - 735
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000379


Abstract: 
Understanding 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 series of compression experiments were performed on hybrid steel-polyvinyl alcohol (PVA) fiber-reinforced concrete containing fly ash and slag powder, with a focus on the fiber content/ratio effect on its compressive behavior; a new approach was built to model the compression behavior of HFRC by using an artificial neural network (ANN) method. The proposed ANN model incorporated 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, 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.

包含粉煤灰和矿渣的钢-聚乙烯醇混杂纤维混凝土的抗压力学性能试验与人工神经网络模型

目的:通过系列室内试验以及建立的人工神经网络(ANN)模型研究钢-聚乙烯醇(PVA)混杂纤维混凝土的抗压力学性能,探究不同纤维含量和纤维混合比对钢-PVA混杂纤维混凝土抗压性能的影响.
创新点:1. 进行了不同纤维含量和纤维混合比的钢-PVA混杂纤维混凝土抗压试验,揭示了钢-PVA混杂纤维混凝土的抗压性能.2. 考虑23项输入参数,建立了ANN模型模拟钢-PVA混杂纤维混凝土的抗压性能,很好地再现了试验结果,并揭示了不同纤维含量和纤维混合比对钢-PVA混杂纤维混凝土抗压性能的影响规律.
方法:1. 通过抗压试验,分析不同纤维含量和纤维混合比对钢-PVA混杂纤维混凝土抗压性能的影响;将试验结果与既有文献数据结合,建立ANN模型的数据库.2. 考虑23项输入参数,建立ANN模型,并进行参数优化、训练和测试以及敏感性分析.3. 对比分析ANN模型和传统损伤模型,验证并论证ANN模型的正确性和优势.4. 通过参数敏感性分析,揭示纤维含量和纤维混合比对钢-PVA混杂纤维混凝土抗压性能的影响.
结论:1. 与损伤模型相比,ANN模型能够更好地模拟钢-PVA混杂纤维混凝土的抗压性能,包括抗压强度、峰值应变和应力-应变曲线.2. ANN模型可以考虑23项混杂纤维混凝土组分的影响,包括纤维特征、混凝土组成成分和素混凝土力学性能.3. 抗压试验结果和ANN模拟结果都表明,钢纤维对混杂纤维混凝土的抗压性能影响大于PVA纤维.

关键词:试验;ANN模型;混杂纤维混凝土;抗压性能;应力-应变曲线

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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