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

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