Structural damage detection using finite element model updating with evolutionary algorithms: a survey

Nizar Faisal Alkayem, Maosen Cao, Yufeng Zhang, Mahmoud Bayat, Zhongqing Su

Research output: Journal article publicationReview articleAcademic researchpeer-review

42 Citations (Scopus)

Abstract

Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.

Original languageEnglish
Pages (from-to)389-411
Number of pages23
JournalNeural Computing and Applications
Volume30
Issue number2
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • Dynamic characteristics
  • Evolutionary algorithms
  • Finite element model updating
  • Optimization
  • Residuals
  • Structural damage detection

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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