A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021): Frattura ed Integrita Strutturale

P. Ghannadi, S.S. Kourehli, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area. © 2023 Parsa Ghannadi.
Original languageEnglish
Pages (from-to)51-76
Number of pages26
JournalFrattura ed Integrita Strutturale
Volume17
Issue number64
DOIs
Publication statusPublished - 2023

Keywords

  • Damage detection
  • Genetic algorithms
  • Particle swarm optimization (PSO)
  • Shape optimization
  • Simulated annealing
  • Structural health monitoring
  • Structural optimization
  • Annealing algorithm
  • Detection problems
  • Finite-element model updating
  • Inverse solution
  • Optimal sensor placement
  • Optimization algorithms
  • Optimization method
  • Particle swarm
  • Structural damage detection
  • Swarm optimization
  • Inverse problems
  • Damage Detection
  • Inverse Problem
  • Simulated Annealing
  • Structural Health Monitoring

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