Prediction of compressive strength of “green” concrete using artificial neural networks

Behzad Omran, Ruoyu Jin, Qian Chen

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

With its growing emphasis on sustainability, the construction industry is more interested in applying environmentally friendly concrete, also known as “gre en” concrete, in its construction projects. Among other benefits, concrete made with alternative or recycled waste material can reduce pollution and energy use, as well as lower the cost of concrete production. However, the impacts of these alternative mat erials on concrete properties have not been fully understood, which limits the wide applications of “green” concrete in practice. This study investigates the application of Artificial Neural Networks (ANN) to predict the compressive strength (CS) of concr ete made with alternative materials such as fly ash, Haydite lightweight aggregate and Portland limestone cement. A feed - forward Multilayer Perceptron (MLP) model was applied for this purpose. To determine the accuracy and flexibility of this approach, two different input methods (relative and numerical) were tested on the generated ANN models. The results showed that concrete made of Portland limestone cement had slightly better CS than concrete made of Portland cement. Generally, both input methods provid ed adequate accuracy to predict CS. It was also observed that a proper MLP model with one hidden layer and sufficient neurons (depending on the input variables and type of cement) could effectively predict the CS of “green” concrete.
Original languageEnglish
Title of host publication50th Annual International Conference of the Associated Schools of Construction
Place of PublicationWashington, D.C.,USA.
Pages0-0
Number of pages1
Publication statusPublished - 11 Apr 2014
Event50th Annual International Conference of the Associated Schools of Construction - Washington, D.C.,USA, March 26-28, 2012
Duration: 11 Apr 2014 → …

Conference

Conference50th Annual International Conference of the Associated Schools of Construction
Period11/04/14 → …

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Compressive strength
Concretes
Neural networks
Cements
Multilayer neural networks
Limestone
Portland cement
Construction industry
Fly ash
Neurons
Sustainable development
Pollution
Costs

Cite this

Omran, B., Jin, R., & Chen, Q. (2014). Prediction of compressive strength of “green” concrete using artificial neural networks. In 50th Annual International Conference of the Associated Schools of Construction (pp. 0-0). Washington, D.C.,USA..
Omran, Behzad ; Jin, Ruoyu ; Chen, Qian. / Prediction of compressive strength of “green” concrete using artificial neural networks. 50th Annual International Conference of the Associated Schools of Construction. Washington, D.C.,USA., 2014. pp. 0-0
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Omran, B, Jin, R & Chen, Q 2014, Prediction of compressive strength of “green” concrete using artificial neural networks. in 50th Annual International Conference of the Associated Schools of Construction. Washington, D.C.,USA., pp. 0-0, 50th Annual International Conference of the Associated Schools of Construction, 11/04/14.

Prediction of compressive strength of “green” concrete using artificial neural networks. / Omran, Behzad; Jin, Ruoyu; Chen, Qian.

50th Annual International Conference of the Associated Schools of Construction. Washington, D.C.,USA., 2014. p. 0-0.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

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Omran B, Jin R, Chen Q. Prediction of compressive strength of “green” concrete using artificial neural networks. In 50th Annual International Conference of the Associated Schools of Construction. Washington, D.C.,USA. 2014. p. 0-0