We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural health monitoring framework. This method is based on the use of hidden Markov models with preselected feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals. A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves from the internal defects. The hybrid method simplifies the modeling problem and provides better performance in the analysis of high stress gradient problems.