Damage Sizing in Self-Sensing Materials Using a Genetic Algorithm-Supplemented Electrical Impedance Tomography Formulation

The piezoresistive effect and electrical impedance tomography (EIT) have been thoroughly explored for structural health monitoring (SHM) and nondestructive evaluation (NDE) applications in civil, mechanical, and aerospace venues. Conveniently, EIT has been used for detecting damage by imaging conductivity changes in piezoresistive nanocomposites and sensing skins. Although EIT can spatially resolve damage better than interpolated resistance change methods, its imaging capabilities are somewhat limited and indistinct to such an extent that it is difficult to infer the actual damage size and shape. In light of this limitation, we present a new methodology for determining damage size and shape in self-sensing piezoresistive materials. Our technique makes use of a genetic algorithm (GA) to inversely compute damage size from boundary voltage measurements and EIT-imaged conductivity changes. In this preliminary study, this new technique is first explored computationally and then experimentally validated. Our initial results show that the proposed GAsupplemented EIT formulation can indeed precisely reconstruct damage shapes. These preliminary results consequently suggest a pathway from mere damage localization via EIT to much more complete damage characterization in self-sensing materials.