Abstract
It is of great significance to timely and accurately forecast the safety state of the bridge as far as the maintenance is concerned. Bayesian forecasting is a method of deriving posterior distribution in accord with the sampling information and prior information, where real time online forecasting is realized by means of recursive algorithm and the stationary assumption. Bayesian dynamic linear model is created to forecast the reliability of the bridge on the basis of the observed stress information of a bridge structure. According to the observed information, the model created is a superposition of constant mean model and seasonal effect model. The analysis of a practical example illustrates that Bayesian dynamic linear modes can provide an accurate real time forecast of the reliability of the bridge
Highlights
The researches on the reliability of bridgeR structure implement reliability parameters to reflect the safety state, not considering the possible changes caused asA the time passes on
As a matter of fact, the safety state of a bridge is intensely relevant to the traffic condition, that is to say compared to the late night when the traffic flow is small
E caused by overload traffic, which makes it important to be informed of the instantaneous safety state of the bridge
Summary
R structure implement reliability parameters to reflect the safety state, not considering the possible changes caused as. E caused by overload traffic, which makes it important to be informed of the instantaneous safety state of the bridge. T besides the entire bridge structure state in light of a healthy operation [1,2,3,4]. C Nowadays the reliability forecast and estimation based on the real time observed information on the bridge structure is. E and deduce the Bayesian posterior probability of the state parameters [5,6,7]. Needless of the stationary assumption, the Rdynamic models can keep updating as the observed information updates and distinguishes itself in the
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