Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Join our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessPolymer Concrete (PC) is a composite material made by fully replacing the cement hydrate binders of conventional cement concrete with polymer binders or liquid resins. As expected, the physico-mechanical properties of PC concrete are governed by the composition of the PC mixture. The present study aims to examine the effect of the aggregate type and of the addition of steel fibers on the mechanical properties of PC. In particular, two PC concrete mixtures, using granite or silica aggregates, have been developed and the effect of the addition of steel fibers has been investigated. The PC mixtures are characterized by mechanical tests such as the compression test, the flexural test, the splitting tensile test and the estimation of the energy absorption. The results of this study demonstrate a relative superiority, in terms of mechanical properties, of the PC made with granite aggregates as compared to that of the silica aggregate mixture. Moreover, the addition of steel fibers on PC mixtures showed a significant increase of the compressive toughness, of the splitting tensile and of the flexural strength, whereas the Young
Panagiotis Asteris, Hamid Naseri, Mohsen Hajihassani, Mehdi Kharghani, Constantin E. Chalioris (2021). On the mechanical characteristics of fiber reinforced polymer concrete. , 12(4), DOI: https://doi.org/10.12989/acc.2021.12.4.271.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2021
Authors
5
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.12989/acc.2021.12.4.271
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access