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http://dx.doi.org/10.12989/cac.2022.30.3.209

Artificial neural network modeling to predict the flexural behavior of RC beams retrofitted with CFRP modified with carbon nanotubes  

Almashaqbeh, Hashem K. (Department of Civil Engineering, Isra University)
Irshidat, Mohammad R. (Center for Advanced Materials (CAM), Qatar University)
Najjar, Yacoub (Department of Civil Engineering, The University of Mississippi)
Elmahmoud, Weam (Department of Civil Engineering, The University of Mississippi)
Publication Information
Computers and Concrete / v.30, no.3, 2022 , pp. 209-224 More about this Journal
Abstract
In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.
Keywords
artificial neural network; carbon nanotubes; CFRP; composites; flexural; RC beams;
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Times Cited By KSCI : 6  (Citation Analysis)
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