All industry branches like aerospace, mechanical and civil engineering are interested in less intrusion and more accuracy failure assessment techniques. They are mostly interested in damages like cracks, delaminations, disbanding, corrosion, etc. Damage detection and assessment technique was developed in this paper. It uses variations in structural wave propagation for undamaged and damaged structure. This Structural Health Monitoring (SHM) method is useful especially in large, complex and inaccessible structures [
]. Based on earlier promising results with this approach [
] a set of laboratory tests were carried out on simple elements like strips. Two kind of materials were used: steel and plexy. Several failure cases were introduced by cutting or drilling the samples. Piezoceramics (PZT) elements were served as transmitters and receivers of elastic waves trough the monitored specimens. During these experiment different groups of excitation signals (continuous sine wave, one, four and six sine wave impulses) and frequency (frequency range from 2 to 50 kHz) were applied to introduce wave to the structure. The numerical models were also created using Finite Element Method (FEM). Defects in the form of a notch were simulated by the removal of selected finite elements from the model. This simulation gave possibility to extend set of damages cases and improved nets generalization properties. In both laboratory and numerical experiments advanced signal processing techniques were adopted. The measured signals were preprocessed by wavelet transform in order to remove noise. Frequency analysis was carried out by Fast Fourier transform (FFT). Replication technique was adopted to experimental data. To realize dependences between input (harmonic frequencies) and output data (height, width and localization of damage) Artificial Neural Networks (ANNs) were used. Several input combinations and nets architectures were tested. Results presented in this paper proved reliability and usefulness of proposed approach.