Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique

Bayesian inference methods typically require a considerable amount of computation time in the calculation of forward models. This limitation restricts the application of Bayesian inference methods for the parameter identification of complex engineering problems. We propose a novel likelihood-free Bayesian inference method for structural parameter identification. An adaptive Gaussian surrogate model (GSM) was integrated with the transitional Markov chain Monte Carlo (MCMC) method for Bayesian inference. The log-likelihood function was approximated with GSM and the transitional MCMC method was used to generate the posterior distribution samples. A response reconstruction technique was combined with the likelihood-free Bayesian inference method for the parameter identification. Both numerical studies and experimental studies were conducted to verify the accuracy and efficiency of the proposed method. The results showed that the proposed method could be used to estimate the posterior probabilities of unknown structural parameters. Additionally, the proposed method was more efficient than the delayed rejection adaptive Metropolis and Gibbs sampling methods.

Automated summary

The article proposes a novel likelihood-free Bayesian inference method for structural parameter identification, which combines an adaptive Gaussian surrogate model with the transitional Markov chain Monte Carlo method. The method is used to estimate posterior probabilities of unknown structural parameters and has been shown to be more efficient than traditional sampling methods.