期刊
CONSTRUCTION AND BUILDING MATERIALS
卷 324, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2022.126689
关键词
CO2 Concrete; Recycled aggregate concrete; Green product; Waste recycling; Energy conservation; Regression analysis; Artificial neural network
资金
- Australian Research Council (ARC)
- Australian Government [DP200100057, IH1501000006, IH200100010]
- Australian Provisional Patent [AU 2019904894]
- Australian Research Council [DP200100057] Funding Source: Australian Research Council
CO2 Concrete is an environmentally friendly construction material that reduces CO2 emissions and conserves natural resources. The compressive strength of CO2 Concrete can be accurately predicted using artificial neural networks.
Concrete is a very effective material for the construction of buildings and infrastructure around the world. Unfortunately, typical concrete is a large contributor to CO2 emissions and consumption of natural reserves. CO2 Concrete allows the mitigation of these downfalls by carbonating recycled aggregate, reducing CO2 emissions, reusing crushed masonry materials and conserving virgin aggregate. CO2 Concrete can also be considered reliable as its compressive strength can be accurately predicted by both regression analysis and artificial neural networks. The artificial neural network created for this paper allow accurate prediction of the compressive strength for CO2 Concrete. The artificial neural network exhibited a strong relationship with the experimental specimens, revealing a multiple R of 0.98 and an R square of 0.95. The artificial neural network was also validated by 22 laboratory validation concrete mixes. The artificial neural network displayed an average error of 1.24 MPa or 3.43% in the validation mixes with 59% of concrete samples within 3% error and 77% being within 5% error. The successful prediction of compressive strength of CO2 Concrete can help a greater mainstream use of the green material.
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