Precise damage shaping in self-sensing composites using electrical impedance tomography and genetic algorithms
نویسندگان
چکیده
Fiber-reinforced composites with nanofiller-modified polymer matrices have immense potential to improve the safety of high-risk engineering structures. These materials are intrinsically self-sensing because their electrical conductivity is affected by deformations and damage. This property, known as piezoresistivity, has been extensively leveraged for conductivity-based damage detection via resistance change methods tomographic imaging techniques such impedance tomography (EIT). Although these very effective at detecting presence damage, they suffer from an inability provide precise information about shape, size, or mechanism. particularly detrimental laminated which can complex failure modes, delaminations, that difficult detect. To end, we herein propose a new technique precisely determining shape size in composites. Our makes use genetic algorithm (GA) integrated realistic physics-based models recover changes imaged EIT. We experimentally validate this on carbon nanofiber (CNF)-modified glass fiber-reinforced (GFRP) laminates considering two specific mechanisms: through-holes (as function number, location) impact-induced delaminations impact energy). results show novel accurately reconstruct multiple radii small 1.19 mm caused low velocity impacts. The reconstructed delamination shapes sizes were shown be much better agreement actual observed using optical microscopy than achievable traditional EIT alone. findings illustrate coupling piezoresistivity spatial inversion strategies enable shaping capabilities composite
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ژورنال
عنوان ژورنال: Structural Health Monitoring-an International Journal
سال: 2022
ISSN: ['1741-3168', '1475-9217']
DOI: https://doi.org/10.1177/14759217221077034