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Predicting stress and strain of FRP-confined square/retangular columns using artificial neural networks

Journal Article


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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 invisible processes inside the ANN.

Publication Date


  • 2014

Citation


  • Pham, T. Minh. & Hadi, M. N. S. (2014). Predicting stress and strain of FRP-confined square/retangular columns using artificial neural networks. Journal of Composites for Construction, 18 (6), 04014019-1-04014019-9.

Scopus Eid


  • 2-s2.0-84905831505

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4366&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3350

Has Global Citation Frequency


Start Page


  • 04014019-1

End Page


  • 04014019-9

Volume


  • 18

Issue


  • 6

Place Of Publication


  • United States

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 invisible processes inside the ANN.

Publication Date


  • 2014

Citation


  • Pham, T. Minh. & Hadi, M. N. S. (2014). Predicting stress and strain of FRP-confined square/retangular columns using artificial neural networks. Journal of Composites for Construction, 18 (6), 04014019-1-04014019-9.

Scopus Eid


  • 2-s2.0-84905831505

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4366&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3350

Has Global Citation Frequency


Start Page


  • 04014019-1

End Page


  • 04014019-9

Volume


  • 18

Issue


  • 6

Place Of Publication


  • United States