A Shape Function-Based Updating Parameters Selection Method for Long-Span Bridges

We present a new updating parameters (UPs) selection method to tackle the bottleneck created by having too many UPs and limited measured data in model updating processing. While the model updating is performed by parameter optimization, an ill-conditioned numerical problem may be encountered or the reliability of the result may be unacceptable if too many parameters are used. The selection of UPs thus becomes a key issue, especially for long-span bridges with finite element models that should be divided into at least hundreds of element numbers. A new method is introduced to reduce the number of UPs and retain their physical significance. In this method, original UPs are described by a few macro-parameters based on shape functions. The model subsequently is updated by a normal optimization algorithm, such as the first-order optimization method. Based on a bridge with a three-span continuous beam and a long-span tie-arch, the optimal effects are investigated, with or without a shape function and using different types of shape functions. The results indicate that the effect of the modal updating based on a shape function is more robust than without shape function and the effect of a linear shape function is better than that of a constant value shape function.

Damage Detection on Typical Aeronautical Structures

The paper pursues the exploration of the feasibility and reliability of current damage detection technologies, evaluating their detection capabilities, environmental factors effects, false alarms rate, adaptability to complex geometries, etc. The method to be used is based on finite element modal updating. Three aspects, as outlined below, are covered: testing samples will be aluminium sheets (0.6m x 0.4m x 1.6mm) strengthened with riveted L-shaped stiffeners. Data will be presented from the undamaged specimens. Secondly, the testing of the samples with damage simulated at different places by temporary removal of specific rivets, thus affecting the overall structural characteristics of the structure. The models used for damage identification methods will be fine tuned to properly detect the simulated damages. Finally, using this information, the paper resumes the capabilities of the method to detect and locate the simulated damage.

Bayesian Fast Fourier Transform Approach for Modal Updating Using Ambient Data

The problem of identification of the modal parameters of a structural model using measured ambient response time histories is addressed. A Bayesian Fast Fourier Transform approach (BFFTA) for modal updating is presented which uses the statistical properties of the Fast Fourier transform (FFT) to obtain not only the optimal values of the updated modal parameters but also their associated uncertainties, calculated from their joint probability distribution. Calculation of the uncertainties of the identified modal parameters is very important when one plans to proceed with the updating of a theoretical finite element model based on modal estimates. The proposed approach requires only one set of response data in contrast to many of the existing frequency-based approaches which require averaging. It is found that the updated PDF can be well approximated by a Gaussian distribution centred at the optimal parameters at which the posterior PDF is maximized. Examples using simulated data are presented to illustrate the proposed method.