A hybrid Particle Swarm Optimization – Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Opt...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Advances in engineering software (1992) Ročník 40; číslo 9; s. 883 - 891
Hlavní autori: Begambre, O., Laier, J.E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.09.2009
Predmet:
Cracked beam
Damage identification
Inverse problems
Non-linear oscillator
Particle Swarm Optimization
Truss structure
Non-linear oscillator
Damage identification
Inverse problems
Truss structure
Particle Swarm Optimization
Cracked beam
ISSN:0965-9978
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder–Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO b). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free–free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2009.01.004