JOURNAL ARTICLE
Performance analysis of cement-based sandwich composites with fiber-reinforced skins and rubberized concrete core from waste tire recycling.
Published In: Journal of Sandwich Structures & Materials, 2026, v. 28, n. 4. P. 625 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sambucci, Matteo; Pini, Tommaso; Tirillò, Jacopo; Valente, Marco 3 of 3
Abstract
This article focuses on the development and characterization of cement-based sandwich composites (SCs) featuring a rubberized cementitious core made from 70% rubber powder and 30% rubber granules derived from waste tires, combined with fiber-reinforced mortar skins incorporating short glass fibers (GF) or nanoclay-treated recycled carbon microfibers (rCF). The SCs achieved lightweight concrete densities (1745–1781 kg/m³) suitable for semi-structural applications and demonstrated enhanced flexural strength (up to 195% increase over rubberized core alone) and impact energy absorption (up to 270% higher than reference mortars). Thermal testing showed that fiber-reinforced skins improved residual strength after high-temperature exposure, although the overall thermal stability was limited by degradation of the rubberized core; SCs also exhibited reduced thermal conductivity due to interfacial delamination disrupting heat transfer. Scanning electron microscopy revealed stronger fiber-matrix bonding for rCF but identified interfacial microcracking between core and skins, highlighting the need for improved skin-core adhesion and optimized fiber dispersion to fully realize the composites’ structural potential.
Additional Information
- Source:Journal of Sandwich Structures & Materials. 2026/05, Vol. 28, Issue 4, p625
- Document Type:Article
- Subject Area:Science
- Publication Date:2026
- ISSN:1099-6362
- DOI:10.1177/10996362251392793
- Accession Number:192559179
- Copyright Statement:Copyright of Journal of Sandwich Structures & Materials is the property of Sage Publications Inc. 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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