Probabilistic fatigue life prediction for RC beams under chloride environment considering the statistical uncertainty by Bayesian updating

Abstract

A probabilistic fatigue life prediction model for RC beams under chloride environment is proposed, and the statistical uncertainty is considered by Bayesian inference to determine and update model parameters. In terms of the sparse fatigue data, the Markov-chain Monte-Carlo (MCMC) method is utilized to conduct the Bayesian updating. The prior distribution and posterior distributions are respectively determined by the data in this study and open references. Results show that the fatigue life under chloride environment is accurately predicted by a probabilistic S-N curve, in which as update points increase, predictions get close to tests and the statistical uncertainty is reduced.