Micromechanics-Based Prediction Models and Experimental Validation on Elastic Modulus of Recycled Aggregate Concrete
Shirong Yan,
Binglei Wang,
Yu Sun and
Boning Lyu
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Shirong Yan: School of Civil Engineering, Shandong University, Jinan 250061, China
Binglei Wang: School of Civil Engineering, Shandong University, Jinan 250061, China
Yu Sun: School of Civil Engineering, Shandong University, Jinan 250061, China
Boning Lyu: School of Civil Engineering, Shandong University, Jinan 250061, China
Sustainability, 2021, vol. 13, issue 20, 1-13
Abstract:
Elastic modulus is one of the most important mechanical properties of concrete (including recycled aggregate concrete), and it has a notable guiding significance for engineering. There is a lack of micromechanical research on the elastic modulus of recycled aggregate concrete. This paper adopts four models based on micromechanics, including the Voigt model, Reuss model, Eshelby method, and Mori–Tanaka method, to predict the elastic modulus of recycled aggregate concrete. The optimal model is determined by comparing the results of the four models with the experimental data. On this basis, some previous prediction methods for the elastic modulus of concrete are employed to be compared with the most satisfactory models in this paper. Several experimental data from the open literature are also utilized to better illustrate the reliability of the prediction models. It is concluded that the Mori–Tanaka method unfailingly produces more accurate predictions compared to other models. It gives the best overall approximation for various data and has extensive effects in predicting the elastic modulus of RAC. This work may be helpful in promoting the development of micromechanics research in recycled aggregate concrete.
Keywords: recycled aggregate concrete (RAC); elastic modulus; recycled coarse aggregate (RCA); micromechanics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:20:p:11172-:d:653012
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