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Finite element model updating

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In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs).

Model updating techniques use the measured data to improve the accuracy of numerical models of structures.

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Essential elements on vibration testing and analysis are explained and these include the domains in which data can be represented.

To determine the membership functions of the updated parameters, an objective function is defined and minimized using two metaheuristic optimization algorithms: ant colony optimization (ACO) and particle swarm optimization (PSO).

A structural example is used to investigate the accuracy of the fuzzy model updating strategy using the PSO and ACO algorithms.

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Analyzing this matrix yields information on the sensitive and insensitive zones of the structure.

Color graphics are available to visualize these different zones and enable a fast optimization of the parameter selection.

It has been applied successfully to large-scale industrial problems and proprietary codes are available based on the techniques explained in simple terms in this article.

A basic introduction to the most important procedures of computational model updating is provided, including tutorial examples to reinforce the reader’s understanding and a large scale model updating example of a helicopter airframe.

Fast-running response surface models that approximate multivariate input/output relationships of time-consuming physical-based computer models enable effective finite element (FE) model updating analyses.

In this paper, a response surface-based FE model updating procedure for civil engineering structures in structural dynamics is presented.