Bayesian updating of infrastructure deterioration models

Deterioration forecasting plays an important role in the infrastructure management process. The precision of facility condition forecasting directly influences the quality of maintenance and rehabilitation decision making. One way to improve the precision of forecasting is by successive updating of deterioration model parameters. A Bayesian approach that uses inspection data for updating facility deterioration models is presented. As an empirical study with bridge deck data indicates, the use of this methodology significantly reduces the uncertainty inherent in condition forecasts.