Issue |
MATEC Web Conf.
Volume 199, 2018
International Conference on Concrete Repair, Rehabilitation and Retrofitting (ICCRRR 2018)
|
|
---|---|---|
Article Number | 11008 | |
Number of page(s) | 5 | |
Section | Concrete Materials Technology | |
DOI | https://doi.org/10.1051/matecconf/201819911008 | |
Published online | 31 October 2018 |
Design and characterization of self-sensing steel fiber reinforced concrete
LMDC, INSA/UPS Génie Civil, 135 Avenue de Rangueil, 31077 Toulouse cedex 04 France
* Corresponding author: ferdians@insa-toulouse.fr
The purpose of this communication is to develop a self-sensing cement composite capable of detecting stress variation in concrete by monitoring its electrical property. The relationship between the electrical properties, i.e. electrical resistance of steel fiber reinforced concrete, and stress under loading as part of self-sensing study is presented in here. Amorphous metallic fibers (AMF) with two different lengths i.e. 10 mm and 30 mm are used as concrete reinforcement at a content of 40 kg/m3. A water to cement ratio of 0.39 was adopted for the mix proportions. Natural fine and coarse siliceous aggregates were used for this research. Superplasticizer was used to achieve the target of workability. The two-probe method is used for measuring electrical properties on cylinder specimens with diameter 100 mm and height 200 mm. The influence of different parameters such as fiber length, frequency of power input, maximum stress and variation of potential input on the sensitivity of the sensing are investigated. The results indicate that the electrical resistance of the concrete decreases reversibly during loading and increases reversibly during unloading. Good sensitivity obtained for the mix using 30 mm AMF length indicates that the addition of this type of fiber into concrete can be suitable to produce a self-sensing cement composite.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.