4.7 Article

A unified model for predicting the compressive strength of recycled aggregate concrete containing supplementary cementitious materials

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 251, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.119752

Keywords

Supplementary cementitious material; Recycled concrete aggregate; Hydration; Pozzolanic reaction; Recycled aggregate concrete; Unified model

Funding

  1. China's National Natural Science Foundation of China [51978280]
  2. Special Funds for Guangdong's Provincial Science and Technology Innovation Strategy [2018A030313814]
  3. Fundamental Research Funds for the Central Universities, China [2018ZD43]
  4. China's State Key Laboratory of Subtropical Building Science [2019ZB22]

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Raising environmental awareness has triggered extensive searching for methods to mitigate impacts of concrete industry on the environment. Feasible solutions include partially replacing nature aggregates by recycled concrete aggregates (RCAs) in new concrete production, and/or using supplementary cementitious materials (SCMs) to substitute a fraction of ordinary Portland cement (OPC) binder. However, SCMs of a given type generally have a broad chemical composition compared to OPC; the inherent inferior properties of RCAs often reduce the mechanical strength of resulting concrete. These two factors pose great challenges to accurately predict the compressive strength of concrete containing SCMs and RCAs. In this study, a unified model is proposed by considering the reactivity of SCM and the physical characteristics of RCAs in a concrete mix and calibrated based on an extensive experimental database containing 654 individual datasets. The proposed model predicts the compressive strength of recycled aggregate concrete containing SCMs not only with a good accuracy but also with strong chemical and physical senses, hence providing a useful design tool and, ultimately, promoting application of such green concrete products and contributing to experimental friendliness. (C) 2019 Elsevier Ltd. All rights reserved.

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