Dynamic Model Updating Using Particle Swarm Optimization Method

TL;DR

This paper introduces a particle swarm optimization method for finite element model updating, demonstrating improved accuracy and speed over simulated annealing and genetic algorithms on an H-shaped structure.

Contribution

The paper presents a novel application of PSO for FE model updating, showing it outperforms existing methods in accuracy and computational efficiency.

Findings

PSO provides the most accurate natural frequency updates.

PSO yields the best correlation of mode shapes to measured data.

PSO achieves faster computation than GA and SA methods.

Abstract

This paper proposes the use of particle swarm optimization method (PSO) for finite element (FE) model updating. The PSO method is compared to the existing methods that use simulated annealing (SA) or genetic algorithms (GA) for FE model for model updating. The proposed method is tested on an unsymmetrical H-shaped structure. It is observed that the proposed method gives updated natural frequencies the most accurate and followed by those given by an updated model that was obtained using the GA and a full FE model. It is also observed that the proposed method gives updated mode shapes that are best correlated to the measured ones, followed by those given by an updated model that was obtained using the SA and a full FE model. Furthermore, it is observed that the PSO achieves this accuracy at a computational speed that is faster than that by the GA and a full FE model which is faster than the SA and a full FE model.