Damage estimation using multi objective genetic algorithms

Faisal Shabbir, Piotr Omenzetter

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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Abstract

It is common to estimate structural damage severity by updating a structural model against experimental responses at different damage states. When experimental results from the healthy and damaged states are available, the updated finite element models corresponding to the two states are compared. Updating of these two models occurs sequentially and independently. However, experimental errors, updating procedure errors, modelling errors and parametric errors may propagate and become aggregated in the damaged model in this approach. In this research, a multi-objective genetic algorithm has been proposed to update both the healthy and damaged models simultaneously in an effort to improve the performance of the damage estimation procedure. Numerical simulations of a simply supported beam damaged at multiple locations with noisy mode shapes were considered and improved model updating results were confirmed. It was found that the proposed method is more efficient in accurately estimating damage severity, less sensitive to discretization as well as experimental errors, and gives the analyst an increased confidence in the model updating and damage estimation results.
Original languageEnglish
Title of host publicationProceedings of the 7th European Workshop on Structural Health Monitoring
Pages1069-1076
Number of pages8
DOIs
Publication statusPublished - 8 Jul 2014
EventEWSHM - 7th European Workshop on Structural Health Monitoring - Nantes, France
Duration: 8 Jul 2014 → …

Conference

ConferenceEWSHM - 7th European Workshop on Structural Health Monitoring
CountryFrance
CityNantes
Period8/07/14 → …

Keywords

  • inverse problems
  • finite elements based SHM
  • estimation

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    Shabbir, F., & Omenzetter, P. (2014). Damage estimation using multi objective genetic algorithms. In Proceedings of the 7th European Workshop on Structural Health Monitoring (pp. 1069-1076) https://doi.org/10.13140/2.1.2732.5765