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Accurate and reliable assessment of transportation infrastructures (e.g. bridges) are crucial for ensuring public safety. Simulation-based engineering analyses can be used to assess and predict the health state of structures. Although some of the structural parameters necessary for such simulations cannot be directly measured, they can be inferred from Non-Destructive Testing data, in a typical inverse problem formulation. However, any prediction based on engineering models will never be an exact representation of reality, since many sources of uncertainties can be present: poor physical representation of the problem (model bias); measurement errors; uncertainty on inferred parameters; etc. Uncertainty quantification is, therefore, of utmost importance to ensure reliability of any decision based on the simulations.
This paper investigates the application of the Modular Bayesian framework to perform structural parameter calibration while estimating a function for the model bias. The data for tests comes from an experimental setup and is represented as a combination of a physics-based model, a model bias term and an additive measurement error (considered to be known). In this approach, the inference problem is divided into 3 modules: the first and the second estimate the optimal hyper-parameters of Gaussian Processes (GP) to replace the physics-based and the model bias term, respectively; the third computes the posterior distribution of the structural parameters of interest, taking into account the GPs from the previous modules. By computing a bias-correction function and calibrating the parameters, the modular framework aims at improving the accuracy of the predictions.