A new Bayesian finite element model updating method based on information fusion of multi-source Markov chains

Abstract

In this paper, we present a new Bayesian model updating method that could overcome the problem of low sampling efficiency and over-reliance on single-chain proposal distribution of traditional Markov chain Monte Carlo algorithm. The delayed rejection and adaptive strategies are introduced in sampling process to obtain a certain number of Markov chains from different proposal distributions, which can independently adjust the variances of proposal distributions and improve the acceptance rate of candidate samples. The abnormal chain detection criterion is adopted to eliminate abnormal Markov chains. Then, the initial variances of different proposal distributions are analogous to the accuracy index of multi-source sensors in the signal domain. And the multi-source sensors grouping weighted fusion algorithm is introduced to fuse the screened Markov chains to approach the posterior probability distribution with high accuracy. The implicit relationship between the parameters to be updated and the responses of the finite element model is fitted by the Kriging surrogate model to improve the computational efficiency. The results of study cases demonstrate that the proposed method has good updating efficiency, excellent updating accuracy, and a higher acceptance rate of samples, which provides a new idea for solving the stochastic model updating.