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.
resonance frequency or mass) as a result of modifying a parameter value.
The coefficients obtained for all combinations of responses and parameters are stored in a sensitivity matrix.
The sensitivity method is probably the most successful of the many approaches to the problem of updating finite element models of engineering structures based on vibration test data.
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.
Finite-element-model updating techniques are then reviewed and these can be broadly categorized into: matrix update methods, sensitivity-based techniques, iterative optimization procedures, Bayesian methods and computational intelligence techniques.
With the response surface at hand, an optimization problem is formulated explicitly.
In this paper, a response surface-based FE model updating procedure for civil engineering structures in structural dynamics is presented.
The key issues to implement such a model updating are discussed such as sampling with design of experiments, selecting the significant updating parameters and constructing a quadratic polynomial response surface.
The proposed procedure is illustrated by a simulated simply supported beam and a full-size precast continuous box girder bridge tested under operational vibration conditions.
The results have been compared with those obtained from the traditional sensitivity-based FE model updating method.