On the Selection and Validation of Component Damage Models for Prediction of Damage-State Behavior of a Truss Bridge

Structural health monitoring has seen significant progress in recent decades and offers major potential benefits in terms of life-cycle management of engineering infrastructure compared to traditional monitoring and maintenance methods. However, many challenges remain, including the lack of availability of sufficient damage-state data from structures of interest on which to validate physics-based models, which can be used to simulate the behavior of structures in their damaged conditions. This can potentially be avoided by validating the damage models at the components or subassemblies where damage would be expected to be found. It is hypothesized that the uncertainty quantified at the component level can then be propagated to the assembly level, thereby avoiding the requirement for damage-state data from the assembly. This chapter presents an investigation of the process described above, where component-level damage-state data is used to select and validate predictive damage models of the struts of a truss bridge, which are then built into an assembly model using dynamic substructuring. The validated assembly-level damage predictions are then compared to a test dataset covering a range of damage conditions in the assembly, and the candidate component-level damage models are compared against each other in terms of accuracy of fit to the test data.