Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Faisal Shabbir, Piotr Omenzetter

Research output: Contribution to journalArticle

66 Citations (Scopus)
6 Downloads (Pure)

Abstract

Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable-stayed bridge using modal data from full-scale dynamic testing.
Original languageEnglish
Pages (from-to)359-375
Number of pages17
JournalComputer Aided Civil and Infrastructure Engineering
Volume30
Issue number5
Early online date9 Sep 2014
DOIs
Publication statusPublished - May 2015

Fingerprint

Particle swarm optimization (PSO)
Global optimization
Footbridges
Cable stayed bridges
Measurement errors
Materials properties
Boundary conditions
Geometry
Testing

Cite this

@article{c9732866df8a411f98d393004f9cdab4,
title = "Particle swarm optimization with sequential niche technique for dynamic finite element model updating",
abstract = "Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable-stayed bridge using modal data from full-scale dynamic testing.",
author = "Faisal Shabbir and Piotr Omenzetter",
year = "2015",
month = "5",
doi = "10.1111/mice.12100",
language = "English",
volume = "30",
pages = "359--375",
journal = "Computer Aided Civil and Infrastructure Engineering",
number = "5",

}

TY - JOUR

T1 - Particle swarm optimization with sequential niche technique for dynamic finite element model updating

AU - Shabbir, Faisal

AU - Omenzetter, Piotr

PY - 2015/5

Y1 - 2015/5

N2 - Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable-stayed bridge using modal data from full-scale dynamic testing.

AB - Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable-stayed bridge using modal data from full-scale dynamic testing.

U2 - 10.1111/mice.12100

DO - 10.1111/mice.12100

M3 - Article

VL - 30

SP - 359

EP - 375

JO - Computer Aided Civil and Infrastructure Engineering

JF - Computer Aided Civil and Infrastructure Engineering

IS - 5

ER -