Damage Identification by Computational Model Updating

Computational model updating techniques have been developed in the past for adjusting selected parameters of large order finite element models in order to make the models compatible with experimental data. Numerical optimization procedures are applied for minimizing the differences of analytical and experimental data, for example, natural frequencies, mode shapes and/or frequency response functions. Since long these techniques have also been investigated with regard to their ability to localize and quantify structural damage. The success of such an approach is mainly governed by the quality of the damage model and its ability to describe the structural property changes due to damage in a physical meaningful way. The change of such parameters identified from test data taken continuously or temporarily over the time may serve as a feature for structural health monitoring. It is well known that low frequency vibration test data or static response data are not very well suited for detecting and quantifying localized small size damage. Exploitable results can only be expected if high spatial resolution of the response data is available. Time domain response data from impact tests carry high frequency information which usually is lost when experimental modal data are utilized for damage identification. Even so only little literature was found addressing the utilization of experimental time histories for model updating in conjunction with damage identification. The theoretical background and examples for using both types of experimental data, high resolution modal data as well as time domain data with high frequency content will be presented in the paper. These data are used in conjunction with a new technique of localizing and quantifying the damage parameters using the test data and the models of the damaged and the undamaged structure simultaneously (“multi model updating”) which is regarded as an attempt to reduce the effects of the unavoidable non-uniqueness of structural damage models.