Symbolization-based differential evolution strategy for identification of structural parameters

Rongshuai Li, Akira Mita, Jin Zhou

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

SUMMARY This new method of identifying structural parameters, called 'symbolization-based differential evolution strategy' (SDES), merges the advantages of symbolic time series analysis and differential evolution (DE). Data symbolization in SDES alleviates the effects of harmful noise. SDES was numerically compared with particle swarm optimization and DE on raw acceleration data. These simulations revealed that SDES provided better estimates of structural parameters when the data were contaminated by noise. We applied SDES to experimental data to assess its feasibility in realistic problems. SDES performed much better than particle swarm optimization and DE on raw acceleration data. The simulations and experiments show that SDES is a powerful tool for identifying unknown parameters of structural systems even when the data are contaminated with relatively large amounts of noise.

Original languageEnglish
Pages (from-to)1255-1270
Number of pages16
JournalStructural Control and Health Monitoring
Volume20
Issue number10
DOIs
Publication statusPublished - 2013 Oct

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Particle swarm optimization (PSO)
Time series analysis
Experiments

Keywords

  • building structures
  • differential evolution
  • particle swarm optimization
  • structural health monitoring
  • symbolic time series analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanics of Materials

Cite this

Symbolization-based differential evolution strategy for identification of structural parameters. / Li, Rongshuai; Mita, Akira; Zhou, Jin.

In: Structural Control and Health Monitoring, Vol. 20, No. 10, 10.2013, p. 1255-1270.

Research output: Contribution to journalArticle

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