Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and char acterize structural damage by combining the impedance-based structural heal th monitoring technique with an artificial neural network. The impedance-ba sed structural health monitoring technique, which utilizes the electromecha nical coupling property of piezoelectric materials, has shown engineering f easibility in a variety of practical field applications. Relying on high fr equency structural excitations (typically > 30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectr ic sensors. In order to quantitatively assess the state of structures, mult iple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high fre quency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper con cludes with experimental examples, investigations on a massive quarter scal e model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.