JOURNAL ARTICLE
NANOMODIFIED SELF-COMPACTING CONCRETE BASED ON RECYCLED AGGREGATES.
Published In: Chemical Problems / Kimya Problemləri, 2025, v. 23, n. 1. P. 125 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Guvalov (Kapanakchi), A. A.; Abbasova, S. i.; Ahmadli, N. Z. 3 of 3
Abstract
The effectiveness study results related to "soft" multi-stage crushing mode of concrete scrap are presented. During the research it was found that the processing of concrete scrap using this technology can significantly improve the characteristics of the secondary concrete aggregate, namely crushability, water absorption and voids. It is achieved by reducing the content of cement bound stones in the secondary crushed stone. Significant volumes of the dispersed material formed as a result of such processing can be used as a fine filler part at production technology of the self-compacting concrete. An optimum organic-mineral additive based on nanoparticle with a superplasticizer, which allows to obtain a homogeneous concrete mixture with additional stabilization properties was used for self-compacting concrete. The stability of the rheological characteristics of the modified cement systems will be insured if an optimum amount of organic-mineral additive is used. It was revealed that 28 days strength of a self-compacting concrete with concrete scrap crushing products content reaches more than 55,6 MPa, hardened under normal conditions and more than 75 MPa after one year under air-dry conditions. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Chemical Problems / Kimya Problemləri. 2025/01, Vol. 23, Issue 1, p125
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:2221-8688
- DOI:10.32737/2221-8688-2025-1-125-130
- Accession Number:181123973
- Copyright Statement:Copyright of Chemical Problems / Kimya Problemləri is the property of Institute of Catalysis & Inorganic Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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