Experimental identification of an uncertain computational dynamical model representing a family of structures

被引：15

作者：

Batou, A. ^{[1
]}

Soize, C. ^{[1
]}

Corus, M. ^{[2
]}

机构：

[1] Univ Paris Est, Lab Modelisat & Simulat Multi Echelle, MSME, CNRS,UMR 8208, F-77454 Marne La Vallee, France

[2] LaMSID, CNRS, EDF, UMR 2832, F-92140 Clamart, France

来源：

COMPUTERS & STRUCTURES | 2011年 / 89卷 / 13-14期

关键词：

Uncertainties; Structural dynamics; Stochastic inverse problem; CHAOS REPRESENTATIONS; PHYSICAL SYSTEMS; MAXIMUM-ENTROPY; RANDOM-FIELDS; PROPAGATION;

D O I：

10.1016/j.compstruc.2011.03.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We are interested in constructing an uncertain computational model representing a family of structures and in identifying this model using a small number of experimental measurements of the first eigenfrequencies. The prior probability model of uncertainties is constructed using the generalized probabilistic approach of uncertainties which allows both system-parameters uncertainties and model uncertainties to be taken into account. The parameters of the prior probability model of uncertainties are separately identified for each type of uncertainties, yielding an optimal prior probability model. The optimal prior stochastic computational model allows a robust analysis for the family of structures to be carried out. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：1440 / 1448

页数：9

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