Reliability updating and prediction of bridge structures based on proof loads and monitored data

Bridge deterioration with time and ever increasing traffic loads raise concerns about reliability of aging bridges. One of the ways to predict reliability of aging bridges is to build reasonable resistance prediction model and load effect prediction model. In this paper, to obtain the predicted resistance, by the truncated method or Bayesian method, the initial resistance probability model is updated with the structural proof loads which are greatly less than the resistance of a bridge, reduce uncertainty in the bridge resistance and so increase the bridge reliability; to predict the time-variant load effects which is treated as a time series, the Bayesian dynamic models (BDMs) are introduced and adopted to predict the structural load effects based on the monitored data (everyday monitored extreme stresses). Finally, with the predicted resistance and load effects, the structural reliability indices are solved and predicted with First Order Second Moment method (FOSM), and three numerical examples are provided to illustrate the feasibility and application of the built prediction model in this paper.