Condition prediction of deteriorating concrete bridges using Bayesian updating

It is well known that the U.S. infrastructure is in need of extensive repai r. To ensure that the scarce resources available for maintaining the U.S. b ridge inventory are spent in an optimal manner, bridge management programs have been mandated by the Federal Highway Administration. However, these pr ograms are mainly based on data from subjective condition assessments and d o not use time-variant bridge reliability for decision making. Many nondest ructive test methods exist for the detailed inspection of bridges. Predicti ons based solely on inspection data may be questionable, particularly if li mitations and errors in the measurement methods that are used are not consi dered. Through the application of Bayesian techniques, information from bot h inspection data and engineering judgment can be combined and used in a ra tional manner to better predict future bridge conditions. In this study, th e influence of inspection updating on time-variant bridge reliability is il lustrated for an existing reinforced concrete bridge. Inspection results ar e combined with prior information in a Bayesian light. The approach is illu strated for a reinforced concrete bridge located near Pueblo, Cole. For thi s bridge the effects of corrosion initiation time and rate on time-variant strength are illustrated using simulation. Inspection results are combined with prior information using Bayesian updating. Time-variant bridge reliabi lity computations are performed using a combined technique of adaptive impo rtance sampling and numerical integration. The approach presented allows ac counting for inspection results in the quantitative assessment of condition of bridges and shows how to incorporate quantitative information into brid ge system and component condition prediction.