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Fiber-reinforced composites with nanofiller-modified polymer matrices haveimmense potential to improve the safety of high-risk engineering structures.These materials are intrinsically self-sensing because their electricalconductivity is affected by deformations and damage. This property, known aspiezoresistivity, has been extensively leveraged for conductivity-based damagedetection via electrical resistance change methods and tomographic imagingtechniques such as electrical impedance tomography (EIT). Although thesetechniques are very effective at detecting the presence of damage, they sufferfrom an inability to provide precise information about damage shape, size, ormechanism. This is particularly detrimental for laminated composites which cansuffer from complex failure modes, such as delaminations, that are difficult todetect. To that end, we herein propose a new technique for preciselydetermining damage shape and size in self-sensing composites. Our techniquemakes use of a genetic algorithm (GA) integrated with realistic physics-baseddamage models to recover precise damage shape from conductivity changes imagedvia EIT. We experimentally validate this technique on carbon nanofiber(CNF)-modified glass fiber-reinforced polymer (GFRP) laminates by consideringtwo specific damage mechanisms: through-holes and delaminations. Our resultsshow that this novel technique can accurately reconstruct multiplethrough-holes with radii as small as 1.19 mm and delaminations caused by lowvelocity impacts. These findings illustrate that coupling piezoresistivity withconductivity-based spatial imaging techniques and physics-based inversionstrategies can enable damage shaping capabilities in self-sensing compositestructures.

Precise Damage Shaping in Self-Sensing Composites Using Electrical Impedance Tomography and Genetic Algorithms