The prediction of carbonation depth for recycled aggregate concrete (RAC) is investigated in this paper. The existing prediction models were evaluated, and it showed that the coefficient of variation (COV) of model error for the existing models is high. By introducing the weighed water absorption of aggregates, the COV of model error can be effectively decreased. Compared with the existing models, the proposed model can predict more accurate carbonation depths. For RAC specimens, compared with the fib model and Xiao and Lei's model-a, the COV of model error of the proposed model is 0.36 which is decreased by 33.3%, and when compared with Xiao and Lei's model-b and Silva et al.’s model, the corresponding decreases are 55.2% and 16.2%. Finally, the proposed model is validated by a 10-year-old carbonation experiment, which indicates that the proposed model is reasonable and can be applied to predict the carbonation depth of RAC.
Cement and Concrete Composites – Elsevier
Published: Apr 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud