General solution for shear strength estimate of RC elements based on panel response

Abstract

The capacity of structural systems subjected to shear loads commonly distinguished by discontinuities such as point loads or supports, or abrupt changes of cross section, where complex fields of stresses and strains are generated, is vital information for design. Four structural systems that present stress concentration due to applied shear loads are commonly short walls, deep beams, corbels and beam-column joints. In the present work a model is developed to predict the shear capacity of these elements based on a panel model that considers average strain and stresses in a reinforced concrete orthotropic material, which covers the section of the structural element subjected to stress concentration. In addition, the panel element complies with the longitudinal equilibrium, by equalizing the applied axial load with the internal stresses of the structural element, requiring constitutive material laws for both concrete and steel reinforcement. The original model that has shown good shear strength prediction requires solving the non-linear equation of vertical equilibrium. Thus, this work eliminates the need to solve the iterative problem for the capacity estimation of four possible limit states (failure of concrete in tension and compression, and yielding of longitudinal web and boundary reinforcement). For that, an expression is calibrated for the strain of the model with respect to relevant parameters, for each limit state, that allow the generation of a non-iterative model. The model results in an average predicted capacity over experimental capacity ratio, , of 1.0 and a COV of 0.25, with similar performance for all four structural systems. When comparing these results with the general model that requires an iterative method, a similar performance is observed, with an average strength ratio and COV of 0.98 and 0.23, respectively. Likewise, in comparison with the ACI 318, the latter shows worse predictions (on average 24% lower) and with greater scatter (on average 28% higher). The expression in AASHTO code presents better correlation than ACI with predictions closer the proposed model.