Finite element model updating using an evolutionary Markov Chain Monte Carlo algorithm

Abstract

One challenge in the finite element model (FEM) updating of a physical system is to estimate the values of the uncertain model variables. For large systems with multiple parameters this requires simultaneous and efficient sampling from multiple a prior unknown distributions. A further complication is that the sampling method is constrained to search within physically realistic parameter bounds. To this end, Markov Chain Monte Carlo (MCMC) techniques are popular methods for sampling from such complex distributions. MCMC family algorithms have previously been proposed for FEM updating. Another approach to FEM updating is to generate multiple random models of a system and let these models evolve over time. Using concepts from evolution theory this evolution process can be designed to converge to a globally optimal model for the system at hand. A number of evolution-based methods for FEM updating have previously been proposed. In this paper, an Evolutionary based Markov chain Monte Carlo (EMCMC) algorithm is proposed to update finite element models. This algorithm combines the ideas of Genetic Algorithms, Simulated Annealing, and Markov Chain Monte Carlo techniques. The EMCMC is global optimisation algorithm where genetic operators such as mutation and crossover are used to design the Markov chain to obtain samples. In this paper, the feasibility, efficiency and accuracy of the EMCMC method is tested on the updating of a real structure.