Inverse Calculation of Displacements in CNF/PU from EIT-Imaged Conductivity Changes

Abstract

Composite materials modified with carbon nanofillers have been thoroughly studied for structural health monitoring (SHM) and damage detection applications because they are piezoresistive and therefore self-sensing. That is, mechanical effects such as strain and damage collocate with conductivity changes within the material. The visualization of strain or damage-induced conductivity changes can then be leveraged for damage identification. To this end, electrical impedance tomography (EIT), has also received considerable attention for SHM because it can non-invasively image spatiallydistributed conductivity changes. Despite the potential of piezoresistivity and EIT for SHM, this approach has an important limitation. EIT can only deduce conductivity changes. Conductivity, however, is not a structurally relevant parameter. From a SHM perspective, it would be much more useful to know the underlying mechanical state of the structure that gives rise to the observed conductivity changes. To achieve this, a novel piezoresistive inversion process is herein presented. This process endeavors to inversely determine the underlying displacement field of a piezoresistive material that results in an observed conductivity change as determined via EIT. The accuracy of this process is experimentally tested on a carbon nanofiber (CNF)/polyurethane (PU) nanocomposite. These preliminary results demonstrate that it is indeed possible to inversely determine the mechanical state of a body from conductivity data.