Abstract
This study aims to examine the feasibility of using steel fibers and carbon nanotubes (CNTs) on developing self-sensing ultra-high-performance concrete (UHPC). For this, four steel fiber types with two different shapes (straight vs. twisted) and three different aspect ratios from 65 to 100 were considered at a fiber volume fraction of 2%. 0.5% by volume of CNTs were simultaneously added. A plain UHPC with only 0.5% CNTs was also fabricated and tested as a control specimen. Test results indicated that the addition of 2% steel fibers was effective in enhancing compressive strength, elastic modulus, tensile strength, and strain capacity of the plain UHPC. The compressive behaviors of UHPC and ultra-high-performance fiber-reinforced concrete (UHPFRC) with CNTs were not predicted based on a fractional change in resistance (FCR) measurement, whereas the total tensile behaviors in terms of both stress-strain and stress-crack opening displacement (COD) curves were quite well simulated based on the FCR measurement and curve-fitting equations suggested. The unintended noise in the FCR of the plain UHPC was largely mitigated by adding the steel fibers, while the highest gauge factor (GF) under tensile load was found for the plain UHPC with CNTs. Using micro steel fibers was more effective in increasing the GF than using macro steel fibers, and better predictive results were obtained for the UHPFRC with straight steel fibers as compared to that with twisted steel fibers.
Original language | English |
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Pages (from-to) | 530-544 |
Number of pages | 15 |
Journal | Construction and Building Materials |
Volume | 185 |
DOIs | |
Publication status | Published - 2018 Oct 10 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1C1B2007589 ).
Publisher Copyright:
© 2018 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction
- Materials Science(all)