An Artificial Neural Network Based Prediction of Mechanical and Durability Characteristics of Sustainable Geopolymer Composite

Abstract

Despite the growing environmental consequences of cement production, geopolymer concrete now has gradually evolved as an ecologically sustainable product. This study experimentally investigates the effect of addition of different proportions (0%, 10%, and 20%) of rice husk ash (RHA) and polypropylene (PP) fibers (0%, 0.1%, and 0.3%) on the mechanical and durability characteristics of fly ash (FA)-based geopolymer mortars. The strength property is assessed by testing the mortar specimen by uniaxial compressive strength and flexural strength while the durability properties were tested with water absorption, water sorptivity, and acid (10% concentration of H2SO4) resistance tests. The experimental findings revealed that the PP fiber addition is not significant in improving the compressive strength, while the addition up to 0.3% by volume had shown good improvement in flexural behavior. Water absorption increases with an increment in the replacement proportion of RHA. Water sorptivity also increases with an increase in RHA substitution levels. Furthermore, an artificial neural network prototype was proposed in this work to forecast the mechanical and durability properties of fiber reinforced FA-RHA blended geopolymer mortar. The ANN architecture was constructed utilizing the mechanical and durability characteristics of FA-RHA blended geopolymer mortar procured through experimental investigation. The RHA substitution proportion, sodium hydroxide (NaOH) liquid concentration, and polypropylene fiber content have been employed as input parameters in the construction of ANN framework. The predicted strength values of mechanical and durability tests achieved from the ANN framework agree well with experiment results. Use of geopolymer mortar has a high potential in repairing the structural elements, and further studies can be done on applying this mortar for the repairs.