4.6 Article

Prediction of the dynamic properties in rubberized concrete

Journal

COMPUTERS AND CONCRETE
Volume 27, Issue 3, Pages 185-197

Publisher

TECHNO-PRESS
DOI: 10.12989/cac.2021.27.3.185

Keywords

rubberized concrete; structural material; dynamic modulus of elasticity; damping ratio; natural frequency; regression analysis; artificial neural network

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Rubberized concrete, considered one of the most important green concrete materials, offers significant advantages in damping vibrations and enhancing energy dissipation in reinforced concrete structures, achieved through replacing natural aggregates with rubber particles. However, there is a need for further research to collect, interpret, and numerically investigate experimental findings in order to provide reliable prediction models for the dynamic properties of rubberized concrete.
Throughout the previous years, many efforts focused on incorporating non-biodegradable wastes as a partial replacement and sustainable alternative for natural aggregates in cement-based materials. Currently, rubberized concrete is considered one of the most important green concrete materials produced by replacing natural aggregates with rubber particles from old tires in a concrete mixture. The main benefits of this material, in addition to its importance in sustainability and waste management, comes from the ability of rubber to considerably damp vibrations, which, when used in reinforced concrete structures, can significantly enhance its energy dissipation and vibration behavior. Nowadays, the literature has many experimental findings that provide an interesting view of rubberized concrete?s dynamic behavior. On the other hand, it still lacks research that collects, interprets, and numerically investigates these findings to provide some correlations and construct reliable prediction models for rubberized concrete?s dynamic properties. Therefore, this study is intended to propose prediction approaches for the dynamic properties of rubberized concrete. As a part of the study, multiple linear regression and artificial neural networks will be used to create prediction models for dynamic modulus of elasticity, damping ratio, and natural frequency.

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