PROBABILISTIC SHEAR STRENGTH MODEL FOR DEEP FLEXURAL MEMBERS BASED ON CONJUGATE PRIOR DISTRIBUTION

Some models have been presented by domestic and foreign scholars for shear capacity of deep reinforced concrete flexural members, but there is still no generally accepted shear theory model. Bayesian theory has been applied to predict and design the bearing capacity for some reinforced concrete members, the reasonability, accuracy and superiority of which have been demonstrated, too. However, the calculation results and accuracy are different for different prior distributions. This paper develops probabilistic shear strength models for reinforced concrete deep flexural members by using of Bayesian multivariate statistical theory based on conjugate prior distribution. These probabilistic models are simplified through the Bayesian parameters removal process. Their performance is confirmed by comparison with 115 collected test results. This shows that the deep flexural members' shear strengths, as obtained by the simplified model based on Bayesian theory, are in good agreement with test results, and they are closer to the experimental values than the results given by models used in many nations’ codes. The simplified models can be used in shear strength prediction and design for deep flexural members.