Skip to Main content Skip to Navigation
Conference papers

Damage Estimation Using Muli-Objective Genetic Algorihtms

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.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-01021233
Contributor : Anne Jaigu <>
Submitted on : Wednesday, July 9, 2014 - 10:24:44 AM
Last modification on : Wednesday, July 9, 2014 - 3:27:05 PM
Long-term archiving on: : Thursday, October 9, 2014 - 11:28:00 AM

File

0078.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01021233, version 1

Collections

Citation

Faisal Shabbir, Piotr Omenzetter. Damage Estimation Using Muli-Objective Genetic Algorihtms. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01021233⟩

Share

Metrics

Record views

239

Files downloads

210