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.
Based on these methods, the most appropriate updated FEM is selected
using the Bayesian approach, a problem that traditional FEM updating has
not addressed. This is found to incorporate engineering judgment into
finite elements systematically through the formulations of prior
distributions. Throughout the text, case studies, specifically designed
to demonstrate the special principles are included. These serve to test
the viability of the new approaches in FEM updating.
Finite Element Model Updating Using Computational Intelligence
Techniques analyses the state of the art in FEM updating critically and
based on these findings, identifies new research directions, making it
of interest to researchers in strucural dynamics and practising
engineers using FEMs. Graduate students of mechanical, aerospace and
civil engineering will also find the text instructive.
