The knowledge about the deteriorating characteristics and future operation conditions in civil infrastructure systems is never complete. Any decision-making framework must include a rational approach for quantifying these uncertainties and their bearing on the decision making process, thought the entire life-cycle of operation. A probability logic approach provides a consistent foundation for this, employing probability models to characterize the relative likelihood of the different system properties. Health monitoring data, when available, may be used to update the probabilistic quantification related to these uncertainties as well the future assessment of the condition of the infrastructure system under consideration. This work presents a Bayesian framework for updating the assessment of bridge infrastructure systems through use of monitoring data. Focus is put on deteriorating characteristics for bridges, which can have a significant impact on the reliability of the system. Stochastic simulation techniques, primarily based on Markov Chain Monte Carlo simulation, are proposed for the Bayesian updating and various models classes are examined for the bridge system. The updating of the relative likelihood for each of the models through monitoring data is also considered. An illustrative example is presented that demonstrates the power of this approach for updating the assessment of an aging overpass concrete bridge and guiding maintenance decisions.

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