Effects of carbon nanomaterial type and amount on self-sensing capacity of cement paste

Doo Yeol Yoo, Ilhwan You, Goangseup Zi, Seung Jung Lee

Research output: Contribution to journalArticlepeer-review


This study investigates the implications of carbon nanomaterial type and amount on the electrical properties of cement paste. For this, five different nanomaterials, i.e., carbon nanotube (CNT), carbon fiber (CF), graphite nanofiber (GNF), graphene (G), and graphene oxide (GO), and two different volume fractions of 0.5 and 1% were considered. In addition, the self-sensing capacity of the cement composites with nanomaterials was evaluated under cyclic compressive loads. Test results indicate that the conductivity of plain cement paste was improved by adding carbon nanomaterials. In most cases, the conductivity of the composites was reduced by an increase in curing age and a decrease in nanomaterial amount, except for CF. The composites with CNTs exhibited the best self-sensing capacity regardless of volume fraction (vf), and the order of self-sensing capacity of the composites at a vf of 1% was CNT > GO ≈ GNF > G. The composites with 0.5 and 1 vol% CFs were determined to be not appropriate for a sensor measuring compressive behaviors. The gauge factor of the composites incorporating 1 vol% CNTs was obtained as 77.2–95.5.

Original languageEnglish
Pages (from-to)750-761
Number of pages12
JournalMeasurement: Journal of the International Measurement Confederation
Publication statusPublished - 2019 Feb

Bibliographical note

Funding Information:
This research was supported by a grant ( 18CTAP-C117247-03 ) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Publisher Copyright:
© 2018 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Effects of carbon nanomaterial type and amount on self-sensing capacity of cement paste'. Together they form a unique fingerprint.

Cite this