Finite-element model updating using swarm intelligence algorithms

In this chapter, several nature-inspired optimization algorithms are used to update finite-element models (FEMs) of structural systems. Usually, the numerical models of real mechanical structures, which are obtained by the FEM approach, give different results compared to the experimental measurements. The mismatch between numerical and experimental results is caused by the variability of the model parameters as well as the mathematical simplifications made during the modeling process. The procedure of correcting the numerical models is known as model updating where several model parameters are adjusted to minimize the error between the measurements and the numerical model. In this chapter, the model-updating procedure is defined as an optimization problem where several swarm intelligence algorithms: particle swarm optimization (PSO), ant colony optimization (ACO) and fish school search (FSS) algorithms are used to update the FEMs of two structural systems: A five degree of freedom (DOF) mass-spring system and an unsymmetrical H-shaped structure with real measurements. The results obtained in this study are compared with the results obtained by the genetic algorithm (GA). As a result, the updating procedures based on FSS, ACO and PSO algorithms give better results than the GA approach. Furthermore, the updating problem, in this chapter, is reformulated as a multiobjective (MO) problem, and a multiobjective PSO (MOPSO) algorithm was used to update the five DOF mass-spring system. The MOPSO algorithm shows promising result in model updating.