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A carbon nanotube strain sensor for structural health monitoring

Open access
Kang, Inpil, Schulz, Mark J, Kim, Jay H, Shanov, Vesselin, Shi, Donglu·2006·Smart Materials and Structures

Abstract

A carbon nanotube polymer material was used to form a piezoresistive strain sensor for structural health monitoring applications. The polymer improves the interfacial bonding between the nanotubes. Previous single walled carbon nanotube buckypaper sensors produced distorted strain measurements because the van der Waals attraction force allowed axial slipping of the smooth surfaces of the nanotubes. The polymer sensor uses larger multi-walled carbon nanotubes which improve the strain transfer, repeatability and linearity of the sensor. An electrical model of the nanotube strain sensor was derived based on electrochemical impedance spectroscopy and strain testing. The model is useful for designing nanotube sensor systems. A biomimetic artificial neuron was developed by extending the length of the sensor. The neuron is a long continuous strain sensor that has a low cost, is simple to install and is lightweight. The neuron has a low bandwidth and adequate strain sensitivity. The neuron sensor is particularly useful for detecting large strains and cracking, and can reduce the number of channels of data acquisition needed for the health monitoring of large structures.

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Metadata

Authors
Kang, I., Schulz, M. J., Kim, J. H., Shanov, V., & Shi, D.
Venue
Smart Materials and Structures
Published
2006-04-25
Volume
15
Issue
3
Pages
737-748
ISSN
  • 0964-1726 (ISSN-L)
  • 1361-665X
OpenAlex
W2004072722
Cited by
1,004
References
0
FWCI
12.37
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A carbon nanotube strain sensor for structural health monitoring. https://app.cochat.ai/papers/10.1088/0964-1726/15/3/009
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Kang, I., Schulz, M. J., Kim, J. H., Shanov, V., & Shi, D. (2006). A carbon nanotube strain sensor for structural health monitoring. Smart Materials and Structures. https://doi.org/10.1088/0964-1726/15/3/009

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.