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Predicting strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression using artificial neural networks

Journal Article


Abstract


  • Numerous research studies experimentally investigated the axial compressive behavior of fiber-reinforced polymer tube confined concrete cylinders in the past two decades. However, only a limited number of research studies developed stress–strain models to predict the strength and strain enhancement ratio of fiber-reinforced polymer tube confined concrete cylinders under axial compression. The available strength and strain enhancement ratio models of fiber-reinforced polymer tube confined concrete cylinders are a function of actual confinement ratio only. This study develops strength and strain enhancement ratio models for circular fiber-reinforced polymer tube confined concrete under axial compression based on artificial neural network analyses using Purelin and Tansig transfer functions. The developed strength and strain enhancement ratio models are functions of actual confinement ratio, orientation of fibers, height to diameter ratio, and axial strain in unconfined concrete at peak axial stress. The formulation and performance evaluation of the developed strength and strain enhancement ratio models are carried out using experimental investigation results of 238 circular fiber-reinforced polymer tube confined concrete under concentric axial compression compiled from a database of 599 fiber-reinforced polymer tube confined concrete specimens. The predictions of the developed strength and strain enhancement ratio models match well with the experimental investigation results of the compiled database. The developed strength and strain enhancement ratio models exhibit smaller statistical errors than the available models in the research studies for predicting the strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression.

Publication Date


  • 2019

Citation


  • Khan, Q. S., Sheikh, M. Neaz. & Hadi, M. N. S. (2019). Predicting strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression using artificial neural networks. Advances in Structural Engineering: an international journal, 22 (6), 1426-1443.

Scopus Eid


  • 2-s2.0-85060234073

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2450

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 1426

End Page


  • 1443

Volume


  • 22

Issue


  • 6

Place Of Publication


  • United States

Abstract


  • Numerous research studies experimentally investigated the axial compressive behavior of fiber-reinforced polymer tube confined concrete cylinders in the past two decades. However, only a limited number of research studies developed stress–strain models to predict the strength and strain enhancement ratio of fiber-reinforced polymer tube confined concrete cylinders under axial compression. The available strength and strain enhancement ratio models of fiber-reinforced polymer tube confined concrete cylinders are a function of actual confinement ratio only. This study develops strength and strain enhancement ratio models for circular fiber-reinforced polymer tube confined concrete under axial compression based on artificial neural network analyses using Purelin and Tansig transfer functions. The developed strength and strain enhancement ratio models are functions of actual confinement ratio, orientation of fibers, height to diameter ratio, and axial strain in unconfined concrete at peak axial stress. The formulation and performance evaluation of the developed strength and strain enhancement ratio models are carried out using experimental investigation results of 238 circular fiber-reinforced polymer tube confined concrete under concentric axial compression compiled from a database of 599 fiber-reinforced polymer tube confined concrete specimens. The predictions of the developed strength and strain enhancement ratio models match well with the experimental investigation results of the compiled database. The developed strength and strain enhancement ratio models exhibit smaller statistical errors than the available models in the research studies for predicting the strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression.

Publication Date


  • 2019

Citation


  • Khan, Q. S., Sheikh, M. Neaz. & Hadi, M. N. S. (2019). Predicting strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression using artificial neural networks. Advances in Structural Engineering: an international journal, 22 (6), 1426-1443.

Scopus Eid


  • 2-s2.0-85060234073

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2450

Has Global Citation Frequency


Number Of Pages


  • 17

Start Page


  • 1426

End Page


  • 1443

Volume


  • 22

Issue


  • 6

Place Of Publication


  • United States