AbstractModels of bridge deck deterioration with improved predictive power can provide bridge management strategies that will optimize allocation of the available, typically limited, budget of transportation agencies in a more efficient manner. In turn, this will lead to improvement in the overall condition of bridges. To this end, this study developed a novel statistical hazard model of bridge deck deterioration using a generalized gamma accelerated failure time model with bridge deck attributes as covariates. Bayesian inference was used to estimate the parameters of the model and will update these parameters as new inspection data become available. The Markov chain Monte Carlo sampling method was used to estimate the posterior distribution of parameters utilizing both uncensored and censored inspection data. The proposed approach was applied to approximately 30 years of in-service performance data inspected from 1985 to 2015 for more than 22,000 bridges in the state of Pennsylvania. The results showed that the model based on the generalized gamma distribution had a high accuracy. Further, the reliability of different attribute values of the physical makeup of the main span of the structure, the main span interaction type, and the rebar coating are quantified based on the model results. The proposed model can help improve predictions of future bridge deck conditions and provide decision-making tools for infrastructure management.