Shear capacity model of rectangular RC columns based on Bayesian update

Abstract

Reinforced concrete (RC) columns are function as structural member resisting vertical loading. The combined effects of vertical loading and transverse loading might induce shear failure of RC columns, posing a significant threat to the structure's integrity. While the accuracy of existing models to determine the shear strength of RC column is crucial for design, there is a need for further refinement. In this context, a study introduces a probabilistic shear capacity model based on the truss-arch theory and incorporating the Bayesian theory Monte Carlo Markov Chain (MCMC) method. This model delineates the shear contribution into four distinct components: concrete, stirrup, axial compression force and longitudinal reinforcement longitudinal reinforcement in the middle of the section parallel to the loading direction. A database, comprising 233 RC column specimen sets, facilitates the model's regression analysis. Calibration is achieved against the density distribution and confidence intervals of the posterior parameters. When set against four established models, the new proposal offers a mean value closely aligned with experimental data and showcases reduced variance. To substantiate the shear mechanism of the proposed model, a thorough contribution of each shear component's contribution is made with those in four existing models: Sezen, ACI 318, Priestley, and GB50010. While the Sezen and ACI 318 models recognize the shear contributions of concrete and stirrup, the Priestley and GB50010 models extend to factor in the beneficial impact of the axial compression force component. Comparative analysis underscores the superior predictive capability of the newly proposed model.