Development and analytical verification of an inelastic reinforced concrete joint model

• A parametric equation is developed to predict the joint shear strength.
• Equation is verified by comparing analytical results with the experimental data.
• The effect of including a joint model to the nonlinear analysis is studied.
• In average 18% of the story drift is due to joint shear distortions.
• When a joint model is not employed, beam plastic hinge rotations are overestimated.

Previous experimental research indicated that beam-to-column connections of reinforced concrete (RC) moment resisting frame structures experience considerable deformations under earthquake loading and these deformations have a major contribution to the story drift of the building. In current analysis and design applications, however, the connection regions are generally modeled as rigid zones and the inelastic behavior of the joint is not considered. This assumption gives rise to an underestimation of the story drifts and hence to an improper assessment of the seismic performance of the structure. In order to implement the effect of shear distortions observed in these regions into the seismic design and analysis of buildings, a model that represents their inelastic behavior needs to be developed. In this study, a parametric model that predicts the joint shear strength versus strain relationship is developed by investigating several previous experimental studies on RC beam-to-column connection subassemblies subjected to cyclic loading and establishing an extensive database. Based on this experimental database, parameters that significantly influence the joint behavior are determined by employing statistical correlation method and these key parameters are assembled to form a joint model. This model is then verified by comparing the results obtained from the dynamic earthquake analysis carried out by using Perform 3D with the experimental ones. Based on the verification results, it is observed that the analysis including the developed joint model improves the prediction of the seismic behavior.