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

Author(s):

Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 3, v. 19
Page(s): 765-780
DOI: 10.1177/1475921719863062
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.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921719863062.
  • About this
    data sheet
  • Reference-ID
    10562320
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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