Damage Identification in Bars with a Wave Propagation Approach and a Hybrid Optimization Method
The formulation and solution of the inverse problem of damage identification based on wave propagation approach are presented. Different damage scenarios for a bar are considered. Time history responses, obtained from pulse-echo synthetic experiments, are used to identify damage position, severity and shape. In order to account for noise corrupted data, different levels of signal to noise ratio – varying from 30 to 0 dB – are introduced. In the identification process, different optimization methods are investigated: the deterministic Levenberg-Marquardt; the stochastic Particle Swarm Optimization; and a hybrid technique combining the aforementioned methods. It is shown that the damage identification procedure built on the wave propagation approach was successful, even for highly corrupted noisy data. Test case results are presented and a few comments on the advantages of deterministic and stochastic methods and their combination are also reported. Finally, an experimental validation of the sequential algebraic algorithm, used for modeling the direct problem, is presented.