Abstract

This paper evaluates the capability of waste iron powder-reinforced cementitious matrices as self-sensing materials in lieu of more expensive carbon fiber and nanoparticle reinforced matrices. Electrical impedance spectroscopy coupled with equivalent circuit modeling is used to determine the bulk resistance of the composite beams containing up to 40% by volume of iron particulates under flexural loading. The fractional change in resistance and the gage factor, as functions of the applied stress, increases with increasing iron particulate content, demonstrating the ability of these composites in self-sensing. A microstructure-guided electro-mechanical finite element model is used to simulate the strain sensing response of these composites. The 2D microstructure is subjected to different applied tensile stresses, and the deformed geometry subjected to an electrical potential to simulate the change in resistance. Debonding at the inclusion-paste interface under load, which is found to significantly influence the fractional change in resistance, is accounted for by using a bilinear softening model. The model is found to correlate well with the experimental data, and has the potential to facilitate microstructural design of materials to achieve desired degrees of self-sensing.

Original languageEnglish (US)
Pages (from-to)225-234
Number of pages10
JournalCement and Concrete Composites
Volume90
DOIs
StatePublished - Jul 1 2018

Fingerprint

Microstructure
Composite materials
Iron
Iron powder
Adhesive pastes
Acoustic impedance
R Factors
Experiments
Debonding
Ointments
Tensile stress
Equivalent circuits
Carbon fibers
Gages
Spectroscopy
Nanoparticles
Geometry
carbon fiber

Keywords

  • Debonding
  • Electrical impedance
  • Microstructural model
  • Particulate reinforcement
  • Strain sensing

ASJC Scopus subject areas

  • Building and Construction
  • Materials Science(all)

Cite this

@article{abdeec89c33e45b49a61bd85635678de,
title = "Strain sensing ability of metallic particulate reinforced cementitious composites: Experiments and microstructure-guided finite element modeling",
abstract = "This paper evaluates the capability of waste iron powder-reinforced cementitious matrices as self-sensing materials in lieu of more expensive carbon fiber and nanoparticle reinforced matrices. Electrical impedance spectroscopy coupled with equivalent circuit modeling is used to determine the bulk resistance of the composite beams containing up to 40{\%} by volume of iron particulates under flexural loading. The fractional change in resistance and the gage factor, as functions of the applied stress, increases with increasing iron particulate content, demonstrating the ability of these composites in self-sensing. A microstructure-guided electro-mechanical finite element model is used to simulate the strain sensing response of these composites. The 2D microstructure is subjected to different applied tensile stresses, and the deformed geometry subjected to an electrical potential to simulate the change in resistance. Debonding at the inclusion-paste interface under load, which is found to significantly influence the fractional change in resistance, is accounted for by using a bilinear softening model. The model is found to correlate well with the experimental data, and has the potential to facilitate microstructural design of materials to achieve desired degrees of self-sensing.",
keywords = "Debonding, Electrical impedance, Microstructural model, Particulate reinforcement, Strain sensing",
author = "Pu Yang and Swaptik Chowdhury and Narayanan Neithalath",
year = "2018",
month = "7",
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language = "English (US)",
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journal = "Cement and Concrete Composites",
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AU - Yang, Pu

AU - Chowdhury, Swaptik

AU - Neithalath, Narayanan

PY - 2018/7/1

Y1 - 2018/7/1

N2 - This paper evaluates the capability of waste iron powder-reinforced cementitious matrices as self-sensing materials in lieu of more expensive carbon fiber and nanoparticle reinforced matrices. Electrical impedance spectroscopy coupled with equivalent circuit modeling is used to determine the bulk resistance of the composite beams containing up to 40% by volume of iron particulates under flexural loading. The fractional change in resistance and the gage factor, as functions of the applied stress, increases with increasing iron particulate content, demonstrating the ability of these composites in self-sensing. A microstructure-guided electro-mechanical finite element model is used to simulate the strain sensing response of these composites. The 2D microstructure is subjected to different applied tensile stresses, and the deformed geometry subjected to an electrical potential to simulate the change in resistance. Debonding at the inclusion-paste interface under load, which is found to significantly influence the fractional change in resistance, is accounted for by using a bilinear softening model. The model is found to correlate well with the experimental data, and has the potential to facilitate microstructural design of materials to achieve desired degrees of self-sensing.

AB - This paper evaluates the capability of waste iron powder-reinforced cementitious matrices as self-sensing materials in lieu of more expensive carbon fiber and nanoparticle reinforced matrices. Electrical impedance spectroscopy coupled with equivalent circuit modeling is used to determine the bulk resistance of the composite beams containing up to 40% by volume of iron particulates under flexural loading. The fractional change in resistance and the gage factor, as functions of the applied stress, increases with increasing iron particulate content, demonstrating the ability of these composites in self-sensing. A microstructure-guided electro-mechanical finite element model is used to simulate the strain sensing response of these composites. The 2D microstructure is subjected to different applied tensile stresses, and the deformed geometry subjected to an electrical potential to simulate the change in resistance. Debonding at the inclusion-paste interface under load, which is found to significantly influence the fractional change in resistance, is accounted for by using a bilinear softening model. The model is found to correlate well with the experimental data, and has the potential to facilitate microstructural design of materials to achieve desired degrees of self-sensing.

KW - Debonding

KW - Electrical impedance

KW - Microstructural model

KW - Particulate reinforcement

KW - Strain sensing

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