For the efficient life-cycle maintenance of bridge, prediction of future bridge performance based on the current performance must be required and it is possible more rational decision-making through the higher accuracy of the prediction model of bridge deterioration. In other words, predicting a performance of bridges is important to reduce the costs of maintenance, repair, rehabilitation and replacement of bridges. To establish the optimal maintenance strategy and planning, there are needs to consider bridge performance history and prediction of maintenance time. In this study, performance models of bridges are developed on the basis of condition index of the bridges by a statistical and probabilistic method. The condition index(or grade) resulted from visual inspection and are associated with bridge damage. (corrosion, fatigue, crack, water leak, scaling, etc.) Factors causing these defects are considered in bridge element performance model. Also, the predicted performance includes a lot of uncertain factors because the condition index of bridge elements were observed by person. So, when inspector determine the bridge condition index(or grade), it is essential to perform In-depth inspection and monitoring using device. While performing a detailed inspection of all parts of a bridge and/or assigning an experienced inspector have reduced a significant part of errors contained in the prediction model, such personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. In this study, to solve this problem, a Bayesian updating method which is related to updating prior information of bridge deterioration is used to the optimal maintenance strategy in Bridge Management System(BMS) considering the uncertainty of inspection data. Also, examples of application are presented, showing the effects of inspection and updating on domestic(Korea) bridge maintenance strategies. That is, we propose a bridge maintenance scenario model based on Bayesian updating and discuss the uncertainty of inspection data obtained from a bridge. The main purpose of this research is to verify advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.