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AuthorKazi, M.-K.
AuthorEljack, F.
AuthorMahdi, E.
Available date2023-09-10T17:35:33Z
Publication Date2020
Publication NameComposite Structures
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.compstruct.2020.112885
URIhttp://hdl.handle.net/10576/47365
AbstractIn this paper, artificial neural network (ANN) models are developed to predict the load-displacement curves for better understanding the behavior of cotton fiber/polyvinyl chloride (PVC) composites. Series of experiments were undertaken in the laboratory for a varying percentage of composite fiber to characteristic loading. Based on those experimental data, the ANN models were trained and tested on the TensorFlow backend using Keras library in Python by implementing the back-propagation method. For better prediction and accuracy of the load-displacement curves, the grid search hyperparameter tuning method was used, followed by k-fold cross-validation. The developed approach proved to be very efficient and reduced the time and effort of the behavioral study for numerous samples, and it will help materials designers to design their future experiments effectively. A similar approach to predict load-displacement curves using ANN can be extended for any kind of composite material if the necessary experimental data are available. 2020 Elsevier Ltd
SponsorThis paper was made possible by NPRP grant No 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherElsevier Ltd
SubjectArtificial neural network
Cotton fiber/PVC composite
Fiber-reinforced polymer
Intelligent product design
Machine learning
TitlePredictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves
TypeArticle
Volume Number254
dc.accessType Abstract Only


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