Research on the strength prediction equation and model of cement stabilized macadam mixed with recycled construction waste aggregate

J Jiangtao Fan C Chen Zhang (Shenzhen Institute for Quantum Science and Engineering, Department of Chemistry, and Department of Physics) Y Yong Yi Y Yu Zhang (Xiangya Hospital, Central South University Changsha China) C Chenfan Bai

Abstract

Abstract While the issue of construction waste siege is becoming increasingly serious, the road construction industry is also facing the problem of sand and stone materials shortage, and adding construction waste to the road base can effectively help address both issues. In this work, the effects of 0–9.5 and 9.5–37.5 mm recycled construction waste aggregate (RCWA) content on the mechanical properties of cement-stabilized macadam (CSM) mixed with the RCWA were investigated, with the optimal RCWA content that allows for a strength not lower than that of ordinary CSM determined. Here, the mechanical strength growth law of CSM mixed with RCWA was studied, while a prediction equation and a model of the mechanical strength of the mix were proposed and the attendant reliability verified. The results indicated that the optimal ratio of CSM mixed with RCWA is as follows: 0–9.5 mm RCWA: 9.5–37.5 mm RCWA: 19.5–37.5 mm natural aggregate: 9.5–19.5 mm natural aggregate = 45:20:29:6. The correlation coefficient R 2 of the established strength prediction equation was as high as 0.98, and when the cement content and RCWA content are known, the mechanical strength can be predicted. Meanwhile, the correlation coefficient R 2 of the proposed strength prediction model was as high as 0.99, and when the 7-day mechanical strength of CSM mixed with RCWA is known, the model can be used to predict the mechanical strength at any curing age. This was verified using laboratory tests, and it was found that the deviation between the predicted values and the actual values was small.

Article Details

Volume / Issue Vol. 15, Issue 1
Published March 20, 2025
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (5)

J

Jiangtao Fan

C

Chen Zhang

Shenzhen Institute for Quantum Science and Engineering, Department of Chemistry, and Department of Physics

Y

Yong Yi

Y

Yu Zhang

Xiangya Hospital, Central South University Changsha China

C

Chenfan Bai