Ground Mixed Recycled Aggregates and Slag Development Using Dual Quaternion Feed Forward Neural Network for Eco-Friendly Geo-polymers

Geopolymer Concrete (GPC) is maintainable construction material which is applied for conventional cement concrete. Various factors impact effectiveness of GPCS like types of binder material utilized, molar concentration of an activator solution as well as preserving conditions. Recently, the recycling of brickwork destruction has attracted growing attention. In this research, development of eco-friendly geopolymers feasibility is identified by the utilization of Ground Mixed Recycled Aggregates (GMRA). In this determination process, the flexural tests as well as compressive according to geopolymers are performing. The slag modification, alkali concentration as well as SiO2/Na2O ratio effects were determined. The relationship among the flexural asset as well as compressive for geopolymer based on GMRA was developed by test data fitting. In that approach, the intensity of the CO2 as well as charge effectiveness of GMRA with geopolymer was minimized as well as enhanced the slag replacement ratio. However, insignificant consequence of alkali concentration on CO2 concentration as well as charge effectiveness is experimented. The data are analyzed using Dual Quaternion Feed Forward Neural Network (DQFFNN) which obtains a less Mean Square Error (MSE) of 0.5432 compared to existing methods like Recurrent Neural Network (RNN).