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    Predicting Stress and Strain of FRP-Confined Square/Rectangular Columns Using Artificial Neural Networks

    Source: Journal of Composites for Construction:;2014:;Volume ( 018 ):;issue: 006
    Author:
    Thong M. Pham
    ,
    Muhammad N. S. Hadi
    DOI: 10.1061/(ASCE)CC.1943-5614.0000477
    Publisher: American Society of Civil Engineers
    Abstract: This study proposes the use of artificial neural networks (ANNs) to calculate the compressive strength and strain of fiber reinforced polymer (FRP)–confined square/rectangular columns. Modeling results have shown that the two proposed ANN models fit the testing data very well. Specifically, the average absolute errors of the two proposed models are less than 5%. The ANNs were trained, validated, and tested on two databases. The first database contains the experimental compressive strength results of 104 FRP confined rectangular concrete columns. The second database consists of the experimental compressive strain of 69 FRP confined square concrete columns. Furthermore, this study proposes a new potential approach to generate a user-friendly equation from a trained ANN model. The proposed equations estimate the compressive strength/strain with small error. As such, the equations could be easily used in engineering design instead of the
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      Predicting Stress and Strain of FRP-Confined Square/Rectangular Columns Using Artificial Neural Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/72276
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    contributor authorThong M. Pham
    contributor authorMuhammad N. S. Hadi
    date accessioned2017-05-08T22:08:46Z
    date available2017-05-08T22:08:46Z
    date copyrightDecember 2014
    date issued2014
    identifier other33423449.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/72276
    description abstractThis study proposes the use of artificial neural networks (ANNs) to calculate the compressive strength and strain of fiber reinforced polymer (FRP)–confined square/rectangular columns. Modeling results have shown that the two proposed ANN models fit the testing data very well. Specifically, the average absolute errors of the two proposed models are less than 5%. The ANNs were trained, validated, and tested on two databases. The first database contains the experimental compressive strength results of 104 FRP confined rectangular concrete columns. The second database consists of the experimental compressive strain of 69 FRP confined square concrete columns. Furthermore, this study proposes a new potential approach to generate a user-friendly equation from a trained ANN model. The proposed equations estimate the compressive strength/strain with small error. As such, the equations could be easily used in engineering design instead of the
    publisherAmerican Society of Civil Engineers
    titlePredicting Stress and Strain of FRP-Confined Square/Rectangular Columns Using Artificial Neural Networks
    typeJournal Paper
    journal volume18
    journal issue6
    journal titleJournal of Composites for Construction
    identifier doi10.1061/(ASCE)CC.1943-5614.0000477
    treeJournal of Composites for Construction:;2014:;Volume ( 018 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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