Finite Element Model Updating Using Computational Intelligence Techniques

Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: multi-layer perceptron neural networks for real-time FEM updating; particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; simulated annealing to put the methodologies into a sound statistical basis; and response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity.