RIS ID
132920
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 Details
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.