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Finite-element model updating using swarm intelligence algorithms

Finite-element model updating using swarm intelligence algorithms

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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.

Chapter Contents:

  • Abstract
  • 27.1 Introduction
  • 27.2 Finite-element method
  • 27.3 Finite-element model updating methods
  • 27.4 The objective function
  • 27.5 Particle swarm optimization
  • 27.5.1 Updating a numerical example using PSO algorithm
  • 27.6 The ant colony optimization (ACO) algorithm for continuous domain
  • 27.6.1 Updating a numerical example using ACO algorithm
  • 27.7 Fish school search
  • 27.7.1 Updating a numerical example using the FSS algorithm
  • 27.8 The unsymmetrical H-shaped structure
  • 27.8.1 Simulation settings
  • 27.8.1.1 Settings for the PSO algorithm
  • 27.8.1.2 Settings for the GA algorithm
  • 27.8.1.3 Settings for the ACO algorithm
  • 27.8.1.4 Settings for the FSS algorithm
  • 27.8.2 The updating results for the unsymmetrical H-shaped structure
  • 27.9 Finite-element model updating using a multiobjective PSO algorithm
  • 27.9.1 Multiobjective PSO algorithm
  • 27.9.2 Updating a numerical example using the MOPSO algorithm
  • 27.10 Conclusion
  • References

Inspec keywords: search problems; particle swarm optimisation; swarm intelligence; structural engineering computing; ant colony optimisation; finite element analysis; genetic algorithms

Other keywords: ACO; model-updating procedure; MOPSO algorithm; particle swarm optimization; structural systems; FEM approach; finite-element models; mechanical structures; GA approach; nature-inspired optimization algorithms; genetic algorithm; FSS algorithms; ant colony optimization; five DOF mass-spring system; fish school search algorithms; multiobjective PSO algorithm; swarm intelligence algorithms

Subjects: Mechanical engineering applications of IT; Civil and mechanical engineering computing; Numerical analysis; Finite element analysis; Optimisation techniques; Artificial intelligence; Optimisation; Expert systems and other AI software and techniques

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