Bayesian history matching for structural dynamics applications. (September 2020) - Gardner, P.; Lord, C.; Barthorpe, R.J.
Highlights: A novel combined Bayesian history matching and Gaussian Process regression approach for calibrating computer models, whilst accounting for model discrepancy, that also infers the functional form of the model discrepancy. The first application of Bayesian history matching – a method for performing calibration whilst accounting for additive model discrepancy – in a structural dynamics context. The method is shown to be effective on an experimental case study of a five storey shear structure under pseudo-damage scenarios, where the inferred model discrepancy functions are demonstrated to provide insight into model improvements. A general formulation of Bayesian history matching that separates the inputs, parameter and outputs within the statistical model. Abstract: Computer models provide useful tools in understanding and predicting quantities of interest for structural dynamics. Although computer models (simulators) are useful for a specific context, each will contain some level of model-form error. These model-form errors arise for several reasons e.g., numerical approximations to a solution, simplifications of known physics, an inability to model all relevant physics etc. These errors form part of model discrepancy ; the difference between observational data and simulator outputs, given the 'true' parameters are known. If model discrepancy is not considered during calibration, any inferred parameters will be biased and predictive performance may be poor. Bayesian history matching (BHM) is a technique for calibrating simulators under the assumption that additive model discrepancy exists. This 'likelihood-free' approach iteratively assesses the input space using emulators of the simulator and identifies parameters that could have 'plausibly' produced target outputs given prior uncertainties. This paper presents, for the first time, the application of BHM in a structural dynamics context. Furthermore, a novel method is provided that utilises Gaussian Process (GP) regression in order to infer the missing model discrepancy functionally from the outputs of BHM. Finally, a demonstration of the effectiveness of the approach is provided for an experimental representative five storey building structure.