Subject to various factors under loading, bridges appear to be discrete. Thus, it is unavoidable to take the practical bridge into consideration with regard to the bridge deflection forecasting. Given this, the Bayesian dynamic forecasting theory is introduced to forecast the bridge deflection. Since the bridge deflection can stay stable in a long term, create constant mean discount Bayesian conditional equation and observational equation and deduce the Bayesian posterior probability of the bridge deflection conditional parameters on the basis of the prior information of the parameters. With recursive deduction, the conditional parameters keep updating as observational data are imported. The results of Bayesian forecasting comprise values and confidence interval, which makes it more instructive. Finally, practical examples are adopted to examine the superior performance of Bayesian dynamic forecasting theory.
