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Innovative predictive model for flexural strength of recycled aggregate concrete from multiple datasets

Innovative predictive model for flexural strength of recycled aggregate concrete from multiple... In the construction industry, concrete has shown to be among the most essential and commonly using construction materials. In the present era of global warming resulting from the over dependence on the use of conventional cements, there has been a necessity to employ green technologies especially in the production of concrete to meet the millennium developmental goals (MDGs) for a more sustainable infrastructure and safer environment. The reason has propelled an investigation into the utilization of recycled aggregate (RA) materials of different consistencies to explore the potentials of their application in concrete production that meets both the structural design standard and MDGs requirements. In this research, an extensive search was undertaken to gather multiple experimental data from research works concentrating efforts around the utilization of recycled aggregates among other elements in eco-friendly concrete production. Those from where 302 datasets were collected had proven the effectiveness of the RAs on improving the flexural strength (fck) of concrete. These data were deployed to propose an intelligent predictive model using the artificial neural network (ANN) of the 9-6-1 network. The database was trained and validated using 70–30% of the data, respectively. The proposed model performance has shown an accuracy of 98.5% with a unique close-form equation, which is rare with the use of ANN. This shows that the developed model can be applied in solving recycled aggregate concrete (RAC) problems both with software manipulation and manually (using the closed-form equation). Generally, overall shows the importance of predicting the production and performance of a greener concrete from recycled aggregates (RAs) of both fine and coarse consistency for sustainable structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Innovative predictive model for flexural strength of recycled aggregate concrete from multiple datasets

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-022-00558-1
Publisher site
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Abstract

In the construction industry, concrete has shown to be among the most essential and commonly using construction materials. In the present era of global warming resulting from the over dependence on the use of conventional cements, there has been a necessity to employ green technologies especially in the production of concrete to meet the millennium developmental goals (MDGs) for a more sustainable infrastructure and safer environment. The reason has propelled an investigation into the utilization of recycled aggregate (RA) materials of different consistencies to explore the potentials of their application in concrete production that meets both the structural design standard and MDGs requirements. In this research, an extensive search was undertaken to gather multiple experimental data from research works concentrating efforts around the utilization of recycled aggregates among other elements in eco-friendly concrete production. Those from where 302 datasets were collected had proven the effectiveness of the RAs on improving the flexural strength (fck) of concrete. These data were deployed to propose an intelligent predictive model using the artificial neural network (ANN) of the 9-6-1 network. The database was trained and validated using 70–30% of the data, respectively. The proposed model performance has shown an accuracy of 98.5% with a unique close-form equation, which is rare with the use of ANN. This shows that the developed model can be applied in solving recycled aggregate concrete (RAC) problems both with software manipulation and manually (using the closed-form equation). Generally, overall shows the importance of predicting the production and performance of a greener concrete from recycled aggregates (RAs) of both fine and coarse consistency for sustainable structures.

Journal

Asian Journal of Civil Engineering – Springer Journals

Published: Jul 1, 2023

Keywords: Sustainable concrete; Green concrete; Flexural strength; Recycled aggregates; Soft computing

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