As bridge infrastructure worldwide continues to age, concerns regarding their safety and functionality have become more pressing. This scenario underscores the imperative for strategies aimed at increasing the service life of bridges through effective maintenance and refurbishment practices. In Korea, the prevailing approach to bridge assessment predominantly hinges on aggregating condition assessment outcomes to assign an overall load carrying capacity grade. This method, however, falls short in capturing the complex system of bridges, potentially overlooking their direct impact on bridge performance. To address this issue, the adoption of a more sophisticated assessment framework is proposed, leveraging the precision of optimization process. This approach integrates both static and dynamic bridge test data to update the finite element model by defining variables at the member level and variables at the system level. The objective is to converge on a unified solution that captures the behavior of the entire bridge structure utilizing a baseline digital twin model for operation and maintenance. This sophisticated methodology enables the creation of highly accurate digital twins of the bridge system, which serve as dynamic repositories for critical bridge data. Such a digital twin functions as a cornerstone for ongoing monitoring and assessment, ensuring that bridge maintenance is both predictive and proactive. Through this innovative approach, bridge authorities can maintain an accurate baseline model, significantly enhancing the precision of bridge evaluations and, by extension, the safety and longevity of bridge infrastructure.