Unified Stress-Strain Model for FRP and Actively Confined Normal-Strength and High-Strength Concrete

Abstract

Accurate modeling of the complete stress-strain relationship of confined and unconfined concrete is of vital importance in predicting the overall flexural behavior of reinforced concrete structures. The analysis-oriented models, which utilize the dilation characteristics of confined concretes for stress-strain relationship prediction, are well recognized for their versatility in such modeling applications. These models assume that at a given lateral strain, the axial compressive stress and strain of fiber-reinforced polymer (FRP)–confined concrete are the same as those of the same concrete when it is actively confined under a confining pressure equal to that supplied by the FRP jacket. However, this assumption has recently been demonstrated experimentally to be inaccurate for high-strength concrete (HSC). It was shown that at a given axial strain, lateral strains of actively confined and FRP-confined concretes of the same concrete strength correspond when they are subjected to the same lateral confining pressure. However, it was also shown that under the same condition, concrete confined by FRP exhibits a lower strength enhancement compared to that seen in companion actively confined concrete. To develop an accurate model that can describe the experimentally observed behavior, two large test databases were assembled for actively confined and FRP-confined concretes through an extensive review of the literature. Based on the analysis of the databases, a new approach is developed to establish the axial stress difference between the actively confined and FRP-confined concretes. Finally, a unified model is proposed to describe the stress-strain relationships of actively confined and FRP-confined concrete. Comparisons with experimental test results show that the predictions of the proposed model are in good agreement with the test results of both actively confined and FRP-confined concrete, and the model provides improved predictions compared to the existing models.