DYNAMIC FINITE ELEMENT MODEL UPDATING USING SIMULATED ANNEALING AND GENETIC ALGORITHMS

Abstract

Dynamic finite element (FE) model updating may be considered as an optimisation process. Over the past few years, two powerful new optimisation algorithms have been developed independently of each other; namely, the genetic algorithm (GA) and simulated annealing (SA). These algorithms are both probabilistic search algorithms capable of finding the global minimum amongst many local minima. This paper compares various implementations of the two algorithms for model updating purposes. A new variant of simulated annealing is suggested and is found to be the most effective of all the optimisation algorithms considered. This version of simulated annealing is then tested using several objective functions for simulated model updating in the frequency domain. In the second part of this paper, both SA and GAs are applied to a practical FE model updating problem using measured data. The new variation of the SA algorithm, termed the blended SA algorithm, performed better than the traditional GA algorithm. However, the results obtained show a significant dependence on the choice of updating parameters. It was concluded that model updating using these optimisation algorithms is a promising and viable approach, but the appropriate choice of updating parameters is of paramount importance.