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dc.creatorAwoyera, Paul
dc.creatorKirgiz, Mehmet S.
dc.creatoramelec, viloria
dc.creatorOvallos, David
dc.description.abstractThere has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model developmentinvolved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceJournal of Materials Research and Technologyspa
dc.subjectArtificial neural networksspa
dc.subjectGenetic programmingspa
dc.subjectSelf-Compacting concretespa
dc.titleEstimating strength properties of geopolymer self-compacting concrete using machine learning techniquesspa
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