Issue |
E3S Web Conf.
Volume 156, 2020
4th International Conference on Earthquake Engineering & Disaster Mitigation (ICEEDM 2019)
|
|
---|---|---|
Article Number | 05028 | |
Number of page(s) | 6 | |
Section | Structure | |
DOI | https://doi.org/10.1051/e3sconf/202015605028 | |
Published online | 02 April 2020 |
Parameter Identification for Modeling Steel Fiber Reinforced Concrete under Compression to Prevent Concrete Cover Spalling under Severe Earthquake Loading Condition
1 ‘Civil Engineering Department, Institut Teknologi Sepuluh Nopember, 60111, Surabaya Indonesia
2 School of Civil and Environmental Engineering, University of New South Wales, NSW, 2052, Sydney Australia
* Corresponding author: piscesa@ce.its.ac.id
The use of steel fiber in concrete material can improves both the strength and the ductility of concrete. The fibers can postpone or mitigate the concrete cover spalling under severe loading conditions such as during an earthquake. In this paper, the behavior of Steel Fiber Reinforced Concrete (SFRC) under compression is modeled using the Attard and Setunge’s stress-strain model. The parameter identification consisted of the elastic modulus (Ec), the peak strength (/cc), the residual strength (fes), and the peak strain of concrete under compression (ecc). From the investigation, it is found that the models proposed for active confined concrete can be applied for steel fiber reinforced concrete. It was also shown that the axial strain at peak stress increases as the fiber volumetric ratio and fiber aspect ratio increased. A simple formula to predict the approximate value of confining pressure to account for the steel fiber presence is proposed. The verification of the proposed model with the experimental results is presented in detail. Furthermore, insight into the performance of the reinforced concrete column made of SFRC using the fiber-based cross-sectional analysis is sighted.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 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.