A FINITE ELEMENT MODEL UPDATING METHOD BASED ON IMPROVED MCMC METHOD

To copy with the shortage of the convergence and computational efficiency in high dimension parameters, a fast finite element model updating method is proposed based on an adaptive fusion algorithm and its relevance vector machine. The posterior distribution for correction parameters is deduced based on the generalized without prejudice prior distribution. An adaptive algorithm is introduced to adjust the bandwidth of the proposal distribution based on Markov Chain Monte Carlo (MCMC) simulation method. Refusal by a delaying algorithm is introduced to improve the new sample acceptance probability. In order to improve the computational efficiency, the regression model between the correction parameters and the theoretical calculation values of the finite element model is established. Simulation and experimental results show that the method owns fast convergence speed and computation efficiency. It is superior to the traditional first-order optimization model modification method, providing a new way to improve the calculation efficiency of uncertainty model updating.