Bayesian model updating in time domain with metamodel-based reliability method

Masaru Kitahara, Sifeng Bi, Matteo Broggi, Michael Beer

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this study, a two-step approximate Bayesian computation (ABC) updating framework using dynamic response data is developed. In this framework, the Euclidian and Bhattacharyya distances are utilized as uncertainty quantification (UQ) metrics to define approximate likelihood functions in the first and second steps, respectively. A new Bayesian inference algorithm combining Bayesian updating with structural reliability methods (BUS) with the adaptive Kriging model is then proposed to effectively execute the ABC updating framework. The performance of the proposed procedure is demonstrated with a seismic-isolated bridge model updating application using simulated seismic response data. This application denotes that the Bhattacharyya distance is a powerful UQ metric with the capability to recreate wholly the distribution of target observations, and the proposed procedure can provide satisfactory results with much reduced computational demand compared with other well-known methods, such as transitional Markov chain Monte Carlo (TMCMC).

Original languageEnglish
Article number0001149
Number of pages11
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume7
Issue number3
Early online date2 Jun 2021
DOIs
Publication statusPublished - 1 Sep 2021

Keywords

  • adaptive Kriging
  • Bayesian model updating
  • Bayesian updating with structural reliability method
  • Bhattacharyya distance
  • metamodeling
  • Stochastic model updating

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