Inverse determination of delamination shape and size in self-sensing composites using electrical impedance tomography and genetic algorithms

Composite materials with nanofiller-modified polymer matrices have received considerable research interest for structural health monitoring (SHM) and nondestructive evaluation (NDE) because they are intrinsically self-sensing. The electrical conductivity of these materials is affected by mechanical stimuli such as strain and damage. Coupled with spatial imaging techniques such as electrical impedance tomography (EIT), this self-sensing property has been extensively leveraged for conductivity-based damage detection. While EIT can spatially resolve damage-induced conductivity changes, it provides little-to-no information about the precise damage shape or size. A major consequence of this is that delaminations, which are a critical failure mode in composite laminates, are difficult to precisely localize and discern from other failure modes. In light of this limitation, we herein present a new methodology for precisely determining the shape and size of delaminations in self-sensing composites. Our novel technique uses a genetic algorithm (GA) to inversely compute delamination shape and size using boundary voltage measurements, EIT-imaged conductivity changes, and a physicsbased delamination model. In the present study, we explore the feasibility of this approach using numerical simulations. Our preliminary results show that this technique can accurately reconstruct delamination shape and size. The proposed methodology can enable precise damage characterization and immensely benefit SHM in self-sensing composites in aerospace, mechanical, and civil applications.