The parametric identification/correction (updating) of dynamic finite element models is here tackled using genetic algorithms. The main advantage with respect to deterministic approaches is a lower sensitivity to measurement noise: this allows using less precise measurement techniques as well as to avoid sophisticated regularisation procedures. The goal of the formulation is to minimise an appropriate combination of the natural frequency error, the correlation error and the response residual. The method is tested by updating a three dimensional steel structure, fixed at one end, whose frequency response is previously measured.
Identificazione parametrica di modelli dinamici agli elementi finiti mediante algoritmi genetici / Fregolent, Annalisa; W., D’Ambrogio. - (1997), pp. 225-230. (Intervento presentato al convegno XIII AIMETA Conference tenutosi a Siena (Italia) nel Ottobre).
Identificazione parametrica di modelli dinamici agli elementi finiti mediante algoritmi genetici
FREGOLENT, Annalisa;
1997
Abstract
The parametric identification/correction (updating) of dynamic finite element models is here tackled using genetic algorithms. The main advantage with respect to deterministic approaches is a lower sensitivity to measurement noise: this allows using less precise measurement techniques as well as to avoid sophisticated regularisation procedures. The goal of the formulation is to minimise an appropriate combination of the natural frequency error, the correlation error and the response residual. The method is tested by updating a three dimensional steel structure, fixed at one end, whose frequency response is previously measured.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
