Failure prediction in self-sensing nanocomposites via genetic algorithm-enabled piezoresistive inversion

Abstract

Conductive nanocomposites have been explored extensively for structural health monitoring (SHM) due to their self-sensing nature via the piezoresistive effect. Combined with a non-invasive conductivity imaging modality such as electrical impedance tomography (EIT), piezoresistivity is a powerful tool for SHM. To date, however, the combination of the piezoresistive effect and EIT has been limited to just damage detection. From a SHM perspective, it may be more beneficial to pre-emptively predict failure before it occurs. To that end, we propose a novel methodology for failure prediction in nanocomposites using piezoresistive inversion. Our approach makes use of a genetic algorithm (GA) to determine the mechanical state of the structure using conductivity changes observed via EIT. First, a rectangular nanocomposite specimen with a central hole is manufactured. Second, the specimen is loaded in tension to induce stress concentrations near the hole. Third, EIT is used to image the resulting stress concentration-induced conductivity changes near the hole. Fourth, GA-enabled piezoresistive inversion is implemented to determine the underlying displacements from the observed conductivity changes. The strains are then determined from kinematic relations and the stresses from constitutive relations. Lastly, a failure criterion is used to predict failure. By validating our results with finite element analysis and digital image correlation, we demonstrate that the proposed approach can accurately predict the onset of failure and therefore enable unprecedented SHM capabilities in piezoresistive nanocomposites.

A computational exploration of the effect of alignment and aspect ratio on alternating current conductivity in carbon nanofiber–modified epoxy

“…Carbon nanofiller–modified polymers have received much attention from the research community for a wide range of potential applications in areas such as electromagnetic shielding (Yousefi et al, 2014), flexible electronics (Cai and Wang, 2015), and self-sensing materials (e.g. robotic or artificial/e-skin (Amjadi et al, 2015), structural health monitoring (Hassan and Tallman, 2019), and human-performance/health wearable sensors (Wang and Loh, 2017)). Key to these applications is the fact that nanofiller modification above the percolation threshold imparts electrical conductivity to the traditionally insulating polymer matrix.…”

Real-Time Precise Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study

“…were selected based on our previous experience with using EIT for imaging of CNF-based polymeric nanocomposites. 6,20,21 Fig. 3 Representative damaged reference mesh (note that damage is randomly generated such that no two reference meshes are the same) and the EIT reconstruction mesh.…”

Damage detection of composite materials via electrical resistance measurement and IR thermography: A review

“…Hassan, H. and T.N. Tallman estimated displacement field of CNT/Epoxy specimen from a reconstructed conductivity map and a given analytical piezoresistivity model correlating strain and conductivity of the material [166]. The author acquired the stress field from constitutive equations with known Young's modulus and Poisson ratio.…”