Probabilistic Calibration for Shear Strength Models of Reinforced Concrete Columns

Abstract

A probabilistic calibration method is proposed for evaluating the accuracy and applicability of available deterministic models for the shear strength of shear-critical reinforced concrete (RC) columns. Shear mechanisms of RC columns were explored and a deterministic model for shear strength was developed based on the variable angle truss-arch model. Subsequently, a probabilistic shear strength model involving both aleatory and epistemic uncertainties was proposed based on the Bayesian theory and the Markov Chain Monte Carlo (MCMC) method. Meanwhile, the probabilistic characteristics of shear strength were determined according to the developed probabilistic shear strength model. Finally, a probabilistic calibration method was developed to evaluate the accuracy and applicability of available shear strength models under different conditions of the compressive strength of concrete, the transverse reinforcement ratio, the axial load ratio, the aspect ratio, and displacement ductility.