Development of a Baseline Digital Twin Model as a Prerequisite for the Digital Twin Definition of a PSC-I Bridge with Model Updating Considering Member Stiffness

Structural health monitoring using various sensors has been widely employed to assess the structural conditions of bridges. In addition, the concept of a digital twin was introduced, which encompasses the life cycle information of a bridge and its real-time data acquisition and utilization. However, the obtained real-time data from sensors primarily reflect the global behavior of the system, making it challenging to identify the root causes of structural changes. For a highly reliable assessment of the global behavior of a bridge, previous history information, that is, a prerequisite model, is required. This study defines a baseline digital twin model (B-DTM) as the stage preceding real-time data utilization in digital twins. The B-DTM is structured into a pre-update phase, which involves the collection of members and system historical data, and a post-update phase, which focuses on model updating. For the case of model updating, due to the inherent complexity of bridge systems, identifying the global optimum for updating remains challenging. In the pre-update phase, a probabilistic approach to historical data such as member stiffness restricts the search domain for model updating, whereas, in the post-update phase, deflection, mode shapes, and natural frequencies derived from load test results representing the real bridge’s behavior are utilized to explore the global optimum solution. The proposed B-DTM was validated using collected data and load test results from a PSC-I girder bridge decommissioned after 45 years of service.

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