STUDY ON PROBABILISTIC SHEAR STRENGTH MODEL FOR DEEP FLEXURAL MEMBERS BASED ON BAYESIAN-MCMC

A shear analytical model for deep beams was investigated considering the influences of objective and subjective uncertainties, and parameters in the probabilistic model of deep beams were simulated based on the R programming language, the process of which introduced Bayesian posteriori parameter estimation theory and the Markov Chain Monte Carlo (MCMC) method. As a result, the most optimal values and the reliability of model parameters were presented, simultaneously a probabilistic shear model for reinforced concrete deep beams was established and a comparison of before and after the model was conducted. Finally, the characteristic shear strengths of deep beams were achieved on the basis of different confidence levels. The research results show that the MCMC method assumed a credible reliability owing to the 50000 times of iterations by which the calculation was obtained, and it was presented that the posteriori probability model possessed a better agreement with test results than that of a prior probability model, while the posteriori model shows less discreteness.