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.
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GA provides an effective solution to both constrained and unconstrained optimization problems by mimicking biological evolution through natural selection.
“GA provides an effective solution to both constrained and unconstrained optimization problems by mimicking the process of biological evolution through natural selection.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [252]
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Self-citation
- Authors
- Hassan, H., & Tallman, T. N.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Hassan, Hashim
- Venue
- Structural Health Monitoring
- Publisher
- SAGE Publications
- Published
- 2019-07-16
- Volume
- 19
- Issue
- 3
- Pages
- 765-780
- ISSN
- 1475-9217 (ISSN-L)
- 1741-3168
- OpenAlex
- W2957316572
- Cited by
- 55
- References
- 43
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 12 · 2021: 6 · 2020: 11 · 2019: 1
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Health Monitoring (publishedVersion)
- Funders
- Purdue University
- Topics
- Physical Sciences › Environmental Science › Pollution › Smart Materials for Construction
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- failure prediction, conductive nanocomposites, electrical impedance tomography, genetic algorithm, stress concentration, displacement estimation, failure criteria, digital image correlation
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