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Comparison between Bayesian updating and approximate Bayesian computation for model identification of masonry towers through dynamic data by Silvia Monchetti; Cecilia Viscardi; Michele Betti; Francesco Clementi is a Engineering article available to read on EtoBox.

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Abstract Model updating procedures based on experimental data are commonly used in case of historic buildings to identify numerical models that are subsequently employed to assess their structural behaviour. The reliability of these models is closely related to their ability to account for all the uncertainties that are involved in the knowledge process. In this regard, to handle these uncertainties and quantify their propagation, Bayesian inference is frequently employed being able to deal with the effects of parameter uncertainty, observation errors and model inadequacy. The computation of the posterior distribution through Bayesian inference needs–however–the evaluation of the likelihood function, which requires solving complex multi-dimensional integration problems. To bridge this shortcoming, the paper compares two Bayesian inference approaches to show how different approximations affect the results of simulated inference: a discrete approach for the likelihood computation in the Bayesian Model Updating (BMU) and a Monte Carlo likelihood-free method known as Approximate Bayesian Computation (ABC) are reported. As reference, the typology of historic masonry towers was considere

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Author

Silvia Monchetti; Cecilia Viscardi; Michele Betti; Francesco Clementi

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Engineering (Physical Sciences)