Computers and Concrete

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Utilising artificial neural networks for prediction of properties of geopolymer concrete

  • Omar A. Shamayleh (School of Civil and Environmental Engineering Faculty of Engineering and Information Technology, University of Technology Sydney (UTS)) ;
  • Harry Far (School of Civil and Environmental Engineering Faculty of Engineering and Information Technology, University of Technology Sydney (UTS))
  • Received : 2022.11.07
  • Accepted : 2023.01.27
  • Published : 2023.04.25
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Abstract

The most popular building material, concrete, is intrinsically linked to the advancement of humanity. Due to the ever-increasing complexity of cementitious systems, concrete formulation for desired qualities remains a difficult undertaking despite conceptual and methodological advancement in the field of concrete science. Recognising the significant pollution caused by the traditional cement industry, construction of civil engineering structures has been carried out successfully using Geopolymer Concrete (GPC), also known as High Performance Concrete (HPC). These are concretes formed by the reaction of inorganic materials with a high content of Silicon and Aluminium (Pozzolans) with alkalis to achieve cementitious properties. These supplementary cementitious materials include Ground Granulated Blast Furnace Slag (GGBFS), a waste material generated in the steel manufacturing industry; Fly Ash, which is a fine waste product produced by coal-fired power stations and Silica Fume, a by-product of producing silicon metal or ferrosilicon alloys. This result demonstrated that GPC/HPC can be utilised as a substitute for traditional Portland cement-based concrete, resulting in improvements in concrete properties in addition to environmental and economic benefits. This study explores utilising experimental data to train artificial neural networks, which are then used to determine the effect of supplementary cementitious material replacement, namely fly ash, Ground Granulated Blast Furnace Slag (GGBFS) and silica fume, on the compressive strength, tensile strength, and modulus of elasticity of concrete and to predict these values accordingly.

Keywords

Acknowledgement

This research is supported by an Australian Government Research Training Program (RTP) Scholarship.

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