Confinement model for LRS FRP-confined concrete using conventional regression and artificial neural network techniques

Abstract

Concrete confined using fiber-reinforced polymer (FRP) composites experience significant enhancements in strength and strain. For the seismic retrofitting of existing reinforced concrete (RC) structures, a large rupture strain (LRS) FRP (i.e., polyethylene terephthalate and naphthalate, denoted as PET and PEN respectively), with a larger rupture strain of more than 5%, is a promising alternative to conventional FRPs with a rupture strain of less than 3%. The majority of analytical models on the stress–strain behavior of FRP-confined concrete under axial compression have focused largely on concrete confined with the traditional FRP material. Analytical research on LRS FRP-confined concrete is, however, limited. Moreover, all existed stress–strain models were determined based on theoretical analysis and test data fitting. In this paper, the artificial neural networks (ANN) method is employed to build a confinement model directly from experimental data to predict the different components of the stress–strain response. A test database consisting of 226 axial compression tests on LRS FRP-confined concrete specimens is used. The test results, in terms of full confined stress–strain response, strength, strain, FRP rupture strain, and dilation response were investigated. Predictive expressions and practical ANN models for the strength, strain, and shape of an axial stress–strain response are provided. Existing models for LRS FRP-confined concrete were also evaluated. The results of the existing and proposed models report that the proposed methods achieve significantly better results.