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Self-sensing concrete enabled by nano-engineered cement-aggregate interfaces

Author(s):


Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 3, v. 16
Page(s): 309-323
DOI: 10.1177/1475921716643867
Abstract:

The objective of this study was to design a multifunctional cement composite that could not only bear loads but also possessed electromechanical properties that are sensitive to damage. A mainstream approach is to disperse large quantities of conductive additives in the cement matrix, which can be costly, involve complex procedures, difficult to scale-up, and degrade concrete’s inherent mechanical properties. Instead, this research proposes a new method to design multifunctional and self-sensing concrete, which is achieved by altering the cement–aggregate interface using conductive, nano-engineered coatings. Here, a carbon nanotube–based ink solution was sprayed onto the surfaces of aggregates and then dried to form electrically conductive, thin film-coated aggregates. Then, the film-coated aggregates were used as is for casting concrete specimens. It was demonstrated experimentally that this procedure yielded concrete specimens that were not only conductive but also had electrical properties that varied in response to applied physical damage. An electrical impedance tomography algorithm was also implemented and used for estimating their spatial resistivity distributions. Since the electrical properties at every location of the film-enhanced concrete were sensitive to damage, electrical impedance tomography was able to produce electrical resistivity maps that indicated the locations and severities of damage. Multiple concrete cylinder, plate, and beam specimens were cast and tested for validating the self-sensing properties of film-enhanced concrete and the spatial damage detection capabilities of the electrical impedance tomography algorithm.

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: 10.1177/1475921716643867.
  • About this
    data sheet
  • Reference-ID
    10561961
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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